System

Abstract

An object of a system according to an embodiment is to efficiently generate an intermediate moving image cut in animation production.SOLUTION: A system according to an embodiment includes an original image analysis unit, a middle division generation unit, an adjustment unit, and a final generation unit. The original image analysis unit analyzes an original image. An intermediate cut generation unit generates an intermediate moving image cut on the basis of the information analyzed by the original image analysis unit. The adjustment unit adjusts the mid-cut moving image cut generated by the mid-cut generation unit. The final generation unit generates a final animation using the intermediate moving image cut adjusted by the adjustment unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques require the manual generation of in-between video cuts in animation production, which is time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to efficiently generate in-between video cuts in animation production. [Means for solving the problem]

[0006] The system according to the embodiment includes an original image analysis unit, an in-between generation unit, an adjustment unit, and a final generation unit. The original image analysis unit analyzes the original image. The in-between generation unit generates in-between video cuts based on information analyzed by the original image analysis unit. The adjustment unit adjusts the in-between video cuts generated by the in-between generation unit. The final generation unit generates a final animation using the in-between video cuts adjusted by the adjustment unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate in-between video cuts in animation production. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The animation generation system according to an embodiment of the present invention uses a generation AI to generate in-between video cuts that connect between original images, thereby enabling the animation generation system to efficiently generate in-between video cuts in animation production.

[0029] An animation generation system according to an embodiment includes an original image analysis unit, an in-between generation unit, an adjustment unit, and a final generation unit. The original image analysis unit analyzes original images. For example, the original image analysis unit analyzes character movements and background changes. The original image analysis unit can also analyze character facial expressions and postures. The original image analysis unit can also analyze the effects of background lighting. The in-between generation unit generates in-between video cuts based on the information analyzed by the original image analysis unit. For example, the in-between generation unit generates in-between video cuts that continuously change character movements. The in-between generation unit can also generate in-between video cuts that take background changes into account. The in-between generation unit can also generate in-between video cuts that take lighting effects into account. The adjustment unit adjusts the in-between video cuts generated by the in-between generation unit. For example, the adjustment unit adjusts the character movements so that they appear natural. The adjustment unit can also adjust background changes so that they appear natural. The adjustment unit can also adjust the effects of lighting so that they appear natural. The final generation unit generates a final animation using the in-between video cuts adjusted by the adjustment unit. For example, the final generation unit checks whether the character's movements are smooth and corrects any unnatural parts. The final generation unit can also check whether the background changes smoothly and correct any unnatural parts. The final generation unit can also check whether the effects of light are smooth and correct any unnatural parts. This allows the animation generation system according to the embodiment to efficiently generate in-between video cuts in animation production. For example, the generation AI can analyze the character's movements and generate in-between video cuts based on that information. The generation AI can also analyze the background changes and generate in-between video cuts based on that information. The generation AI can also analyze the effects of light and generate in-between video cuts based on that information.

[0030] The original image analysis unit analyzes the background information of the original image and can perform an analysis that takes into account changes in the background and the effects of light. For example, the original image analysis unit uses a generation AI to analyze the background information of the original image and generate movements that take into account changes in the background. For example, if the background is moving, the character's movements are adjusted to match that movement. The original image analysis unit also takes into account the effects of light, and the generation AI analyzes the character's shadows and light reflections. For example, it changes the position and shape of the character's shadow depending on the position of the light source. The original image analysis unit also analyzes detailed information about the background and reflects this in the character's movements. For example, if there is wind blowing in the background, it adjusts the movement of the character's hair and clothing to match the wind. This makes it possible to perform an analysis that takes into account changes in the background and the effects of light.

[0031] The original drawing analysis unit analyzes the movement of a character's clothing and accessories in detail, allowing for more realistic movement to be reproduced. For example, the generation AI in the original drawing analysis unit analyzes the movement of a character's clothing and accessories in detail to reproduce realistic movement. For example, it reproduces the movement of clothing fluttering in the wind when a character runs. In addition, the generation AI in the original drawing analysis unit performs physical simulations to analyze the movement of clothing and accessories. For example, it reproduces the way clothing moves according to gravity when a character jumps. In addition, the original drawing analysis unit analyzes the movement of a character's clothing and accessories and reflects this in the animation. For example, it reproduces the detailed movement of accessories held by a character when they move. This allows for a detailed analysis of the movement of a character's clothing and accessories to reproduce realistic movement.

[0032] The original drawing analysis unit uses a 3D model when inputting the original drawing, allowing for hybrid analysis of 2D and 3D. For example, the generation AI in the original drawing analysis unit uses both the 2D information of the original drawing and the 3D model to perform analysis. For example, it simulates the character's movement using a 3D model and reflects the results in the 2D animation. To perform hybrid analysis of 2D and 3D, the generation AI maps the movement of the 3D model onto the 2D original drawing. For example, it generates the movement of a 2D character based on the movement of the 3D model. The original drawing analysis unit also uses a 3D model, allowing the generation AI to analyze the complex movements of the character. For example, it simulates the character's rotation using a 3D model and reflects the results in the 2D animation. This makes hybrid analysis of 2D and 3D possible.

[0033] The original drawing analysis unit can analyze different animation styles and generate inbetween segments suitable for each style. For example, the original drawing analysis unit uses a generation AI to analyze hand-drawn style original drawings and generate inbetween segments suitable for that style. For example, it reproduces the line thickness and texture of hand-drawn style. The original drawing analysis unit also analyzes CG-style original drawings and the generation AI generates inbetween segments suitable for that style. For example, it reproduces the smooth movement and light reflections of CG style. The original drawing analysis unit also analyzes different animation styles and the generation AI uses style conversion technology to generate inbetween segments suitable for each style. For example, it converts hand-drawn style original drawings into CG style drawings and generates inbetween segments suitable for that style. This makes it possible to generate inbetween segments suitable for different animation styles.

[0034] The in-between generation unit simulates the movement of a character's muscles and skeleton when generating in-between frames, allowing for more natural movements to be reproduced. For example, the generation AI of the in-between generation unit simulates the movement of a character's muscles and skeleton to generate natural in-between video cuts. For example, it reproduces the movement of muscles when a character runs. In addition, the in-between generation unit uses the generation AI to perform physical simulations to simulate the movement of muscles and skeletons. For example, it reproduces the movement of skeletons when a character jumps. In addition, the in-between generation unit analyzes the movement of a character's muscles and skeleton and reflects that movement in the in-between video cuts. For example, it reproduces the movement of muscles when a character swings their arms. This allows for the simulation of the movement of a character's muscles and skeleton to reproduce natural movements.

[0035] The in-between generation unit takes into account background movement and changes in light when generating in-between cuts, maintaining the consistency of the entire scene. For example, the generation AI of the in-between generation unit generates in-between video cuts by taking into account background movement and changes in light. For example, if the background is moving, the character's movement is adjusted to match that movement. The in-between generation unit also takes changes in light into account, with the generation AI analyzing the character's shadow and light reflection. For example, it changes the position and shape of the character's shadow depending on the position of the light source. The in-between generation unit also analyzes detailed information about the background and reflects this in the character's movement. For example, if there is wind blowing in the background, it adjusts the movement of the character's hair and clothing to match the wind. This allows the consistency of the scene to be maintained by taking into account background movement and changes in light.

[0036] The in-between generation unit can add viewpoints from different camera angles when generating in-between cuts, thereby generating in-between cuts from multiple viewpoints. In the in-between generation unit, for example, the generation AI adds viewpoints from different camera angles and generates in-between video cuts from multiple viewpoints. For example, the in-between generation unit generates character movements from viewpoints from the front, side, and rear. The in-between generation unit also generates in-between video cuts by taking different camera angles into consideration. For example, the in-between generation unit reproduces the rotating movement of a character from multiple viewpoints. The in-between generation unit also builds a system in which the generation AI generates in-between video cuts using multiple camera angles. For example, the character movements are generated from viewpoints from above and below. This allows the generation AI to add viewpoints from different camera angles and generate in-between cuts from multiple viewpoints.

[0037] The in-between generation unit reproduces the movement of a character's clothing and accessories in detail when generating in-between frames, enabling more realistic animation. For example, the in-between generation unit uses a generation AI to reproduce the movement of a character's clothing and accessories in detail, generating realistic in-between video cuts. For example, it reproduces the movement of a character's clothing fluttering in the wind when the character runs. In addition, the in-between generation unit uses a generation AI to perform physical simulations to reproduce the movement of clothing and accessories. For example, it reproduces the way clothing moves according to gravity when a character jumps. In addition, the in-between generation unit analyzes the movement of a character's clothing and accessories and reflects this in the animation. For example, it reproduces the detailed movement of an accessory held by a character when it moves. This reproduces the movement of a character's clothing and accessories in detail, enabling more realistic animation.

[0038] The adjustment unit fine-tunes the timing and speed of a character's movements when adjusting the in-between frames, allowing for more natural movements. For example, the adjustment unit causes the generation AI to fine-tune the timing and speed of a character's movements, generating natural in-between video cuts. For example, the adjustment unit fine-tunes the movement of a character's feet when running. The adjustment unit also analyzes the timing of a character's movements, allowing the generation AI to adjust the in-between video cuts to match that timing. For example, the adjustment unit fine-tunes the movement of a character when jumping. The adjustment unit also analyzes the speed of the movement, allowing the generation AI to adjust the in-between video cuts to match that speed. For example, the adjustment unit fine-tunes the speed when a character swings their arms. This allows for fine-tuning the timing and speed of a character's movements, allowing for more natural movements.

[0039] The adjustment unit takes into account background movement and changes in light when adjusting in-between frames, maintaining the consistency of the entire scene. For example, the generation AI adjusts in-between video cuts by taking background movement and changes in light into account. For example, if the background is moving, the character's movement is adjusted to match that movement. The adjustment unit also takes changes in light into account, with the generation AI analyzing the character's shadow and light reflection. For example, it changes the position and shape of the character's shadow depending on the position of the light source. The adjustment unit also analyzes detailed information about the background and reflects this in the character's movement. For example, if there is wind blowing in the background, it adjusts the movement of the character's hair and clothing to match the wind. This allows the consistency of the scene to be maintained by taking into account background movement and changes in light.

[0040] The adjustment unit can make adjustments suitable for different animation styles when adjusting in-between frames. For example, the adjustment unit uses a generation AI to adjust hand-drawn style in-between video cuts to recreate movements suitable for that style. For example, the adjustment unit adjusts the thickness and texture of the hand-drawn lines. The adjustment unit also adjusts CG style in-between video cuts to recreate movements suitable for that style. For example, the adjustment unit adjusts smooth CG style movements and light reflections. The adjustment unit also analyzes different animation styles, and the generation AI uses style conversion technology to adjust in-between video cuts suitable for each. For example, the adjustment unit converts hand-drawn style in-between video cuts to CG style and adjusts movements suitable for that style. This makes it possible to adjust in-between frames to suit different animation styles.

[0041] The adjustment unit also fine-tunes the movement of a character's clothing and accessories when adjusting the in-between frames, enabling more realistic animation. For example, the generation AI in the adjustment unit fine-tunes the movement of a character's clothing and accessories to generate realistic in-between video cuts. For example, the generation AI adjusts the movement of a character's clothing in the wind when the character runs. The adjustment unit also performs physical simulations to adjust the movement of clothing and accessories. For example, it adjusts the way clothing moves in accordance with gravity when a character jumps. The adjustment unit also analyzes the movement of a character's clothing and accessories and reflects this in the animation. For example, it adjusts the detailed movement of an accessory held by a character when it moves. This allows the movement of a character's clothing and accessories to be fine-tuned, enabling more realistic animation.

[0042] The final generation unit simulates the movement of a character's muscles and skeleton when generating the final animation, allowing for more natural movements to be reproduced. In the final generation unit, for example, a generation AI simulates the movement of a character's muscles and skeleton to generate a natural final animation. For example, it reproduces the movement of muscles when a character runs. In addition, the final generation unit uses a generation AI to perform physical simulations to simulate the movement of muscles and skeletons. For example, it reproduces the movement of skeletons when a character jumps. In addition, the final generation unit analyzes the movement of a character's muscles and skeleton and reflects that movement in the final animation. For example, it reproduces the movement of muscles when a character swings their arms. This allows for the simulation of the movement of a character's muscles and skeleton to reproduce natural movements.

[0043] The final generation unit takes into account background movement and changes in light when generating the final animation, maintaining consistency across the entire scene. For example, the generation AI in the final generation unit generates the final animation by taking into account background movement and changes in light. For example, if the background is moving, the character's movement is adjusted to match that movement. The final generation unit also takes changes in light into account, with the generation AI analyzing the character's shadow and light reflection. For example, it changes the position and shape of the character's shadow depending on the position of the light source. The final generation unit also analyzes detailed information about the background and reflects this in the character's movement. For example, if there is wind blowing in the background, it adjusts the movement of the character's hair and clothing to match the wind. This allows the scene to maintain consistency by taking into account background movement and changes in light.

[0044] The final generation unit can add viewpoints from different camera angles when generating the final animation, thereby generating animation from multiple viewpoints. In the final generation unit, for example, the generation AI adds viewpoints from different camera angles and generates final animation from multiple viewpoints. For example, the generation AI generates character movements from viewpoints from the front, side, and rear. The final generation unit also generates the final animation by taking different camera angles into consideration. For example, the rotational movement of a character is reproduced from multiple viewpoints. The final generation unit also builds a system in which the generation AI generates the final animation using multiple camera angles. For example, the generation AI generates character movements from viewpoints from above and below. This allows the generation AI to add viewpoints from different camera angles and generate final animation from multiple viewpoints.

[0045] The final generation unit reproduces the movements of a character's clothing and accessories in detail when generating the final animation, allowing for more realistic animation. In the final generation unit, for example, the generation AI reproduces the movements of a character's clothing and accessories in detail to generate a realistic final animation. For example, it reproduces the movement of clothing fluttering in the wind when a character runs. In addition, the final generation unit performs physical simulation to reproduce the movements of clothing and accessories. For example, it reproduces the way clothing moves according to gravity when a character jumps. In addition, the final generation unit analyzes the movements of a character's clothing and accessories and reflects them in the animation. For example, it reproduces the detailed movement of accessories held by a character when they move. This reproduces the movements of a character's clothing and accessories in detail, allowing for a realistic final animation.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The animation generation system can further include an audio analysis unit. The audio analysis unit analyzes the lines of a character and background sounds, and generates in-between video cuts based on that information. For example, it adjusts the mouth movements of a character when they speak to match the sound. The audio analysis unit can also analyze changes in background sounds and generate movements in response to those changes. For example, if the background sound is the sound of wind, it can reproduce the movement of a character's hair or clothing fluttering in the wind. The audio analysis unit can also adjust the character's movements to match the rhythm of the music. This makes it possible to generate animation that takes audio information into account.

[0048] The animation generation system may further include a user customization unit. The user customization unit generates in-between video cuts based on customization information specified by the user. For example, the user can change the character's clothing or hairstyle. The user customization unit can also generate in-between video cuts based on movement patterns specified by the user. For example, the user can specify that the character's movements should be more dynamic. The user customization unit can also generate in-between video cuts based on background and lighting settings specified by the user. This makes it possible to generate animation according to the user's preferences.

[0049] The animation generation system can further include a database unit. The database unit accumulates past animation data and generates in-between video cuts based on that data. For example, it extracts character movement patterns from past animation data and generates new in-between video cuts based on those patterns. The database unit can also extract patterns of background and lighting changes from past animation data and generate in-between video cuts based on those patterns. Furthermore, the database unit can analyze past animation data and provide an algorithm for generating optimal in-between video cuts. This enables efficient animation generation using past data.

[0050] The animation generation system may further include a feedback unit. The feedback unit collects feedback from users and generates in-between video cuts based on the feedback. For example, if the user feels that a character's movement is unnatural, the feedback unit adjusts the movement based on the feedback. The feedback unit may also generate in-between video cuts that reflect improvements specified by the user. For example, if the user feels that a background change is unnatural, the feedback unit adjusts the background change based on the feedback. The feedback unit may also analyze the user's feedback and provide an algorithm for generating optimal in-between video cuts. This makes it possible to generate animation that reflects the user's feedback.

[0051] The animation generation system may further include a real-time rendering unit. The real-time rendering unit renders in-between video cuts in real time and provides an instant preview. For example, it may render character movements in real time, allowing the user to check the movements. The real-time rendering unit may also render changes in background and lighting in real time, allowing the user to check the changes. Furthermore, the real-time rendering unit may reflect customization information specified by the user in real time and provide an instant preview of the results. This allows animation generation while obtaining real-time feedback.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The original drawing analysis unit analyzes the original drawing. For example, it analyzes the character's movements, changes in the background, the character's facial expressions and posture, and the influence of background lighting. Step 2: The in-between generation unit generates in-between video cuts based on the information analyzed by the original image analysis unit. For example, it generates in-between video cuts that continuously change the character's movement, or in-between video cuts that take into account changes in the background and the effects of light. Step 3: The adjustment unit adjusts the in-between video cuts generated by the in-between generation unit, for example, adjusting the character movements, background changes, and lighting effects so that they appear natural. Step 4: The final generation section generates the final animation using the in-between video cuts adjusted by the adjustment section. For example, the section checks whether the character movements, background changes, and lighting effects are smooth, and corrects any unnatural parts.

[0054] (Example 2) The animation generation system according to an embodiment of the present invention uses a generation AI to generate in-between video cuts that connect between original images, thereby enabling the animation generation system to efficiently generate in-between video cuts in animation production.

[0055] An animation generation system according to an embodiment includes an original image analysis unit, an in-between generation unit, an adjustment unit, and a final generation unit. The original image analysis unit analyzes original images. For example, the original image analysis unit analyzes character movements and background changes. The original image analysis unit can also analyze character facial expressions and postures. The original image analysis unit can also analyze the effects of background lighting. The in-between generation unit generates in-between video cuts based on the information analyzed by the original image analysis unit. For example, the in-between generation unit generates in-between video cuts that continuously change character movements. The in-between generation unit can also generate in-between video cuts that take background changes into account. The in-between generation unit can also generate in-between video cuts that take lighting effects into account. The adjustment unit adjusts the in-between video cuts generated by the in-between generation unit. For example, the adjustment unit adjusts the character movements so that they appear natural. The adjustment unit can also adjust background changes so that they appear natural. The adjustment unit can also adjust the effects of lighting so that they appear natural. The final generation unit generates a final animation using the in-between video cuts adjusted by the adjustment unit. For example, the final generation unit checks whether the character's movements are smooth and corrects any unnatural parts. The final generation unit can also check whether the background changes smoothly and correct any unnatural parts. The final generation unit can also check whether the effects of light are smooth and correct any unnatural parts. This allows the animation generation system according to the embodiment to efficiently generate in-between video cuts in animation production. For example, the generation AI can analyze the character's movements and generate in-between video cuts based on that information. The generation AI can also analyze the background changes and generate in-between video cuts based on that information. The generation AI can also analyze the effects of light and generate in-between video cuts based on that information.

[0056] The original drawing analysis unit can estimate the emotion of a character in an original drawing and analyze the character's movements based on that emotion. For example, the generation AI in the original drawing analyzes the character's facial expression and posture to estimate the emotion. For example, it determines that a smiling character has the emotion of joy and analyzes the movement based on that emotion. In addition, to estimate the character's emotion, the generation AI analyzes facial features and body movements in detail. For example, it identifies the emotion based on the movement of the eyebrows and the shape of the mouth, and analyzes the movement according to that emotion. Furthermore, the original drawing analysis unit performs analysis to make the character's movements more natural based on the emotion estimation results. For example, it determines that a character with the emotion of sadness moves slowly and reproduces that movement. This makes it possible to analyze the character's movements based on their emotions.

[0057] The original image analysis unit analyzes the background information of the original image and can perform an analysis that takes into account changes in the background and the effects of light. For example, the original image analysis unit uses a generation AI to analyze the background information of the original image and generate movements that take into account changes in the background. For example, if the background is moving, the character's movements are adjusted to match that movement. The original image analysis unit also takes into account the effects of light, and the generation AI analyzes the character's shadows and light reflections. For example, it changes the position and shape of the character's shadow depending on the position of the light source. The original image analysis unit also analyzes detailed information about the background and reflects this in the character's movements. For example, if there is wind blowing in the background, it adjusts the movement of the character's hair and clothing to match the wind. This makes it possible to perform an analysis that takes into account changes in the background and the effects of light.

[0058] The original drawing analysis unit analyzes the movement of a character's clothing and accessories in detail, allowing for more realistic movement to be reproduced. For example, the generation AI in the original drawing analysis unit analyzes the movement of a character's clothing and accessories in detail to reproduce realistic movement. For example, it reproduces the movement of clothing fluttering in the wind when a character runs. In addition, the generation AI in the original drawing analysis unit performs physical simulations to analyze the movement of clothing and accessories. For example, it reproduces the way clothing moves according to gravity when a character jumps. In addition, the original drawing analysis unit analyzes the movement of a character's clothing and accessories and reflects this in the animation. For example, it reproduces the detailed movement of accessories held by a character when they move. This allows for a detailed analysis of the movement of a character's clothing and accessories to reproduce realistic movement.

[0059] The original drawing analysis unit uses a 3D model when inputting the original drawing, allowing for hybrid analysis of 2D and 3D. For example, the generation AI in the original drawing analysis unit uses both the 2D information of the original drawing and the 3D model to perform analysis. For example, it simulates the character's movement using a 3D model and reflects the results in the 2D animation. To perform hybrid analysis of 2D and 3D, the generation AI maps the movement of the 3D model onto the 2D original drawing. For example, it generates the movement of a 2D character based on the movement of the 3D model. The original drawing analysis unit also uses a 3D model, allowing the generation AI to analyze the complex movements of the character. For example, it simulates the character's rotation using a 3D model and reflects the results in the 2D animation. This makes hybrid analysis of 2D and 3D possible.

[0060] The original drawing analysis unit can analyze different animation styles and generate inbetween segments suitable for each style. For example, the original drawing analysis unit uses a generation AI to analyze hand-drawn style original drawings and generate inbetween segments suitable for that style. For example, it reproduces the line thickness and texture of hand-drawn style. The original drawing analysis unit also analyzes CG-style original drawings and the generation AI generates inbetween segments suitable for that style. For example, it reproduces the smooth movement and light reflections of CG style. The original drawing analysis unit also analyzes different animation styles and the generation AI uses style conversion technology to generate inbetween segments suitable for each style. For example, it converts hand-drawn style original drawings into CG style drawings and generates inbetween segments suitable for that style. This makes it possible to generate inbetween segments suitable for different animation styles.

[0061] The original drawing analysis unit uses the emotion estimation function to analyze the emotions of the character in the original drawing in real time and generate movements that correspond to the emotions. For example, the original drawing analysis unit uses a generation AI to analyze the emotions of the character in the original drawing in real time and generate movements that correspond to those emotions. For example, a character with the emotion of anger will make violent movements. The original drawing analysis unit also uses the emotion estimation function to analyze the character's emotions and generate movements based on those emotions. For example, a character with the emotion of sadness will make slow movements. The original drawing analysis unit also builds a system that analyzes emotions in real time and the generation AI will generate movements that correspond to those emotions. For example, a character with the emotion of joy will make light and nimble movements. This makes it possible to generate movements that correspond to the character's emotions.

[0062] The in-between generation unit can estimate a character's emotions and generate in-between video cuts based on those emotions. For example, the in-between generation unit uses a generation AI to estimate a character's emotions and generate in-between video cuts based on those emotions. For example, a character feeling happy will make light and nimble movements. The in-between generation unit also uses an emotion estimation function to analyze a character's emotions and generate in-between video cuts according to those emotions. For example, a character feeling angry will make intense movements. The in-between generation unit also analyzes a character's emotions in real time and builds a system that generates in-between video cuts based on those emotions. For example, a character feeling sad will make slow movements. This makes it possible to generate in-between video cuts based on a character's emotions.

[0063] The in-between generation unit simulates the movement of a character's muscles and skeleton when generating in-between frames, allowing for more natural movements to be reproduced. For example, the generation AI of the in-between generation unit simulates the movement of a character's muscles and skeleton to generate natural in-between video cuts. For example, it reproduces the movement of muscles when a character runs. In addition, the in-between generation unit uses the generation AI to perform physical simulations to simulate the movement of muscles and skeletons. For example, it reproduces the movement of skeletons when a character jumps. In addition, the in-between generation unit analyzes the movement of a character's muscles and skeleton and reflects that movement in the in-between video cuts. For example, it reproduces the movement of muscles when a character swings their arms. This allows for the simulation of the movement of a character's muscles and skeleton to reproduce natural movements.

[0064] The in-between generation unit takes into account background movement and changes in light when generating in-between cuts, maintaining the consistency of the entire scene. For example, the generation AI of the in-between generation unit generates in-between video cuts by taking into account background movement and changes in light. For example, if the background is moving, the character's movement is adjusted to match that movement. The in-between generation unit also takes changes in light into account, with the generation AI analyzing the character's shadow and light reflection. For example, it changes the position and shape of the character's shadow depending on the position of the light source. The in-between generation unit also analyzes detailed information about the background and reflects this in the character's movement. For example, if there is wind blowing in the background, it adjusts the movement of the character's hair and clothing to match the wind. This allows the consistency of the scene to be maintained by taking into account background movement and changes in light.

[0065] The in-between generation unit can add viewpoints from different camera angles when generating in-between cuts, thereby generating in-between cuts from multiple viewpoints. In the in-between generation unit, for example, the generation AI adds viewpoints from different camera angles and generates in-between video cuts from multiple viewpoints. For example, the in-between generation unit generates character movements from viewpoints from the front, side, and rear. The in-between generation unit also generates in-between video cuts by taking different camera angles into consideration. For example, the in-between generation unit reproduces the rotating movement of a character from multiple viewpoints. The in-between generation unit also builds a system in which the generation AI generates in-between video cuts using multiple camera angles. For example, the character movements are generated from viewpoints from above and below. This allows the generation AI to add viewpoints from different camera angles and generate in-between cuts from multiple viewpoints.

[0066] The in-between generation unit reproduces the movement of a character's clothing and accessories in detail when generating in-between frames, enabling more realistic animation. For example, the in-between generation unit uses a generation AI to reproduce the movement of a character's clothing and accessories in detail, generating realistic in-between video cuts. For example, it reproduces the movement of a character's clothing fluttering in the wind when the character runs. In addition, the in-between generation unit uses a generation AI to perform physical simulations to reproduce the movement of clothing and accessories. For example, it reproduces the way clothing moves according to gravity when a character jumps. In addition, the in-between generation unit analyzes the movement of a character's clothing and accessories and reflects this in the animation. For example, it reproduces the detailed movement of an accessory held by a character when it moves. This reproduces the movement of a character's clothing and accessories in detail, enabling more realistic animation.

[0067] The in-between generation unit uses an emotion estimation function to generate in-between video cuts that correspond to the character's emotions, making it possible to visually express changes in emotions. For example, the in-between generation unit uses a generation AI to estimate the character's emotions and generate in-between video cuts based on those emotions. For example, a character feeling happy will make light and nimble movements. The in-between generation unit also uses the emotion estimation function to analyze the character's emotions and generate in-between video cuts that correspond to those emotions. For example, a character feeling angry will make intense movements. The in-between generation unit also analyzes the character's emotions in real time, building a system that generates in-between video cuts based on those emotions. For example, a character feeling sad will make slow movements. This makes it possible to generate in-between video cuts that correspond to the character's emotions and visually express changes in emotions.

[0068] The adjustment unit can estimate a character's emotions and adjust in-between video cuts based on those emotions. For example, the adjustment unit uses a generation AI to estimate a character's emotions and adjusts in-between video cuts based on those emotions. For example, a character with the emotion of joy will make light and nimble movements. The adjustment unit also uses an emotion estimation function to analyze a character's emotions and adjusts in-between video cuts according to those emotions. For example, a character with the emotion of anger will make violent movements. The adjustment unit also builds a system that analyzes a character's emotions in real time and adjusts in-between video cuts based on those emotions. For example, a character with the emotion of sadness will make slow movements. This makes it possible to adjust in-between video cuts based on a character's emotions.

[0069] The adjustment unit fine-tunes the timing and speed of a character's movements when adjusting the in-between frames, allowing for more natural movements. For example, the adjustment unit causes the generation AI to fine-tune the timing and speed of a character's movements, generating natural in-between video cuts. For example, the adjustment unit fine-tunes the movement of a character's feet when running. The adjustment unit also analyzes the timing of a character's movements, allowing the generation AI to adjust the in-between video cuts to match that timing. For example, the adjustment unit fine-tunes the movement of a character when jumping. The adjustment unit also analyzes the speed of the movement, allowing the generation AI to adjust the in-between video cuts to match that speed. For example, the adjustment unit fine-tunes the speed when a character swings their arms. This allows for fine-tuning the timing and speed of a character's movements, allowing for more natural movements.

[0070] The adjustment unit takes into account background movement and changes in light when adjusting in-between frames, maintaining the consistency of the entire scene. For example, the generation AI adjusts in-between video cuts by taking background movement and changes in light into account. For example, if the background is moving, the character's movement is adjusted to match that movement. The adjustment unit also takes changes in light into account, with the generation AI analyzing the character's shadow and light reflection. For example, it changes the position and shape of the character's shadow depending on the position of the light source. The adjustment unit also analyzes detailed information about the background and reflects this in the character's movement. For example, if there is wind blowing in the background, it adjusts the movement of the character's hair and clothing to match the wind. This allows the consistency of the scene to be maintained by taking into account background movement and changes in light.

[0071] The adjustment unit can make adjustments suitable for different animation styles when adjusting in-between frames. For example, the adjustment unit uses a generation AI to adjust hand-drawn style in-between video cuts to recreate movements suitable for that style. For example, the adjustment unit adjusts the thickness and texture of the hand-drawn lines. The adjustment unit also adjusts CG style in-between video cuts to recreate movements suitable for that style. For example, the adjustment unit adjusts smooth CG style movements and light reflections. The adjustment unit also analyzes different animation styles, and the generation AI uses style conversion technology to adjust in-between video cuts suitable for each. For example, the adjustment unit converts hand-drawn style in-between video cuts to CG style and adjusts movements suitable for that style. This makes it possible to adjust in-between frames to suit different animation styles.

[0072] The adjustment unit also fine-tunes the movement of a character's clothing and accessories when adjusting the in-between frames, enabling more realistic animation. For example, the generation AI in the adjustment unit fine-tunes the movement of a character's clothing and accessories to generate realistic in-between video cuts. For example, the generation AI adjusts the movement of a character's clothing in the wind when the character runs. The adjustment unit also performs physical simulations to adjust the movement of clothing and accessories. For example, it adjusts the way clothing moves in accordance with gravity when a character jumps. The adjustment unit also analyzes the movement of a character's clothing and accessories and reflects this in the animation. For example, it adjusts the detailed movement of an accessory held by a character when it moves. This allows the movement of a character's clothing and accessories to be fine-tuned, enabling more realistic animation.

[0073] The adjustment unit uses the emotion estimation function to adjust the in-between video cuts according to the character's emotion, making it possible to visually express changes in emotion. For example, the adjustment unit uses a generation AI to estimate the character's emotion and adjusts the in-between video cuts based on that emotion. For example, a character feeling happy will make light and nimble movements. The adjustment unit also uses the emotion estimation function to analyze the character's emotion and adjusts the in-between video cuts according to that emotion. For example, a character feeling angry will make intense movements. The adjustment unit also builds a system that analyzes the character's emotion in real time and adjusts the in-between video cuts based on that emotion. For example, a character feeling sad will make slow movements. In this way, the in-between video cuts can be adjusted according to the character's emotion, making it possible to visually express changes in emotion.

[0074] The final generation unit can estimate a character's emotions and generate a final animation based on those emotions. For example, the final generation unit uses a generation AI to estimate a character's emotions and generate a final animation based on those emotions. For example, a character with the emotion of joy will make light and nimble movements. The final generation unit also uses an emotion estimation function to analyze a character's emotions and generate a final animation based on those emotions. For example, a character with the emotion of anger will make intense movements. The final generation unit also builds a system that analyzes a character's emotions in real time and generates a final animation based on those emotions. For example, a character with the emotion of sadness will make slow movements. This makes it possible to generate a final animation based on a character's emotions.

[0075] The final generation unit simulates the movement of a character's muscles and skeleton when generating the final animation, allowing for more natural movements to be reproduced. In the final generation unit, for example, a generation AI simulates the movement of a character's muscles and skeleton to generate a natural final animation. For example, it reproduces the movement of muscles when a character runs. In addition, the final generation unit uses a generation AI to perform physical simulations to simulate the movement of muscles and skeletons. For example, it reproduces the movement of skeletons when a character jumps. In addition, the final generation unit analyzes the movement of a character's muscles and skeleton and reflects that movement in the final animation. For example, it reproduces the movement of muscles when a character swings their arms. This allows for the simulation of the movement of a character's muscles and skeleton to reproduce natural movements.

[0076] The final generation unit takes into account background movement and changes in light when generating the final animation, maintaining consistency across the entire scene. For example, the generation AI in the final generation unit generates the final animation by taking into account background movement and changes in light. For example, if the background is moving, the character's movement is adjusted to match that movement. The final generation unit also takes changes in light into account, with the generation AI analyzing the character's shadow and light reflection. For example, it changes the position and shape of the character's shadow depending on the position of the light source. The final generation unit also analyzes detailed information about the background and reflects this in the character's movement. For example, if there is wind blowing in the background, it adjusts the movement of the character's hair and clothing to match the wind. This allows the scene to maintain consistency by taking into account background movement and changes in light.

[0077] The final generation unit can add viewpoints from different camera angles when generating the final animation, thereby generating animation from multiple viewpoints. In the final generation unit, for example, the generation AI adds viewpoints from different camera angles and generates final animation from multiple viewpoints. For example, the generation AI generates character movements from viewpoints from the front, side, and rear. The final generation unit also generates the final animation by taking different camera angles into consideration. For example, the rotational movement of a character is reproduced from multiple viewpoints. The final generation unit also builds a system in which the generation AI generates the final animation using multiple camera angles. For example, the generation AI generates character movements from viewpoints from above and below. This allows the generation AI to add viewpoints from different camera angles and generate final animation from multiple viewpoints.

[0078] The final generation unit reproduces the movements of a character's clothing and accessories in detail when generating the final animation, allowing for more realistic animation. In the final generation unit, for example, the generation AI reproduces the movements of a character's clothing and accessories in detail to generate a realistic final animation. For example, it reproduces the movement of clothing fluttering in the wind when a character runs. In addition, the final generation unit performs physical simulation to reproduce the movements of clothing and accessories. For example, it reproduces the way clothing moves according to gravity when a character jumps. In addition, the final generation unit analyzes the movements of a character's clothing and accessories and reflects them in the animation. For example, it reproduces the detailed movement of accessories held by a character when they move. This reproduces the movements of a character's clothing and accessories in detail, allowing for a realistic final animation.

[0079] The final generation unit uses an emotion estimation function to generate a final animation according to the character's emotion, making it possible to visually express changes in emotion. In the final generation unit, for example, a generation AI estimates the character's emotion and generates a final animation based on that emotion. For example, a character with the emotion of joy will make light and nimble movements. The final generation unit also uses the emotion estimation function to analyze the character's emotion and generate a final animation according to that emotion. For example, a character with the emotion of anger will make intense movements. The final generation unit also builds a system that analyzes the character's emotion in real time and generates a final animation based on that emotion. For example, a character with the emotion of sadness will make slow movements. This makes it possible to generate a final animation according to the character's emotion and visually express changes in emotion.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The animation generation system can further include an audio analysis unit. The audio analysis unit analyzes the lines of a character and background sounds, and generates in-between video cuts based on that information. For example, it adjusts the mouth movements of a character when they speak to match the sound. The audio analysis unit can also analyze changes in background sounds and generate movements in response to those changes. For example, if the background sound is the sound of wind, it can reproduce the movement of a character's hair or clothing fluttering in the wind. The audio analysis unit can also adjust the character's movements to match the rhythm of the music. This makes it possible to generate animation that takes audio information into account.

[0082] The animation generation system may further include a user customization unit. The user customization unit generates in-between video cuts based on customization information specified by the user. For example, the user can change the character's clothing or hairstyle. The user customization unit can also generate in-between video cuts based on movement patterns specified by the user. For example, the user can specify that the character's movements should be more dynamic. The user customization unit can also generate in-between video cuts based on background and lighting settings specified by the user. This makes it possible to generate animation according to the user's preferences.

[0083] The animation generation system can further include a database unit. The database unit accumulates past animation data and generates in-between video cuts based on that data. For example, it extracts character movement patterns from past animation data and generates new in-between video cuts based on those patterns. The database unit can also extract patterns of background and lighting changes from past animation data and generate in-between video cuts based on those patterns. Furthermore, the database unit can analyze past animation data and provide an algorithm for generating optimal in-between video cuts. This enables efficient animation generation using past data.

[0084] The animation generation system may further include a feedback unit. The feedback unit collects feedback from users and generates in-between video cuts based on the feedback. For example, if the user feels that a character's movement is unnatural, the feedback unit adjusts the movement based on the feedback. The feedback unit may also generate in-between video cuts that reflect improvements specified by the user. For example, if the user feels that a background change is unnatural, the feedback unit adjusts the background change based on the feedback. The feedback unit may also analyze the user's feedback and provide an algorithm for generating optimal in-between video cuts. This makes it possible to generate animation that reflects the user's feedback.

[0085] The animation generation system may further include a real-time rendering unit. The real-time rendering unit renders in-between video cuts in real time and provides an instant preview. For example, it may render character movements in real time, allowing the user to check the movements. The real-time rendering unit may also render changes in background and lighting in real time, allowing the user to check the changes. Furthermore, the real-time rendering unit may reflect customization information specified by the user in real time and provide an instant preview of the results. This allows animation generation while obtaining real-time feedback.

[0086] The animation generation system can further use the emotion estimation function to generate audio effects based on the character's emotions. For example, if the character is angry, an intense audio effect corresponding to that emotion can be generated. If the character is sad, a quiet audio effect corresponding to that emotion can be generated. Furthermore, if the character is happy, a bright audio effect corresponding to that emotion can be generated. This allows audio effects to be generated according to the character's emotions, enhancing the realism of the animation.

[0087] The animation generation system can further use an emotion estimation function to adjust colors based on the character's emotion. For example, if the character is angry, a reddish color corresponding to that emotion can be applied. If the character is sad, a bluish color corresponding to that emotion can be applied. Furthermore, if the character is happy, a bright color corresponding to that emotion can be applied. This allows for color adjustment according to the character's emotion and enhances the visual expression.

[0088] The animation generation system can further use emotion estimation functionality to generate camerawork based on the character's emotions. For example, if a character is angry, intense camerawork corresponding to that emotion can be applied. If a character is sad, quiet camerawork corresponding to that emotion can also be applied. Furthermore, if a character is happy, light camerawork corresponding to that emotion can also be applied. This allows for the generation of camerawork that corresponds to a character's emotions and enhances visual expression.

[0089] The animation generation system can further use an emotion estimation function to select music based on the character's emotion. For example, if the character is angry, intense music corresponding to that emotion can be selected. If the character is sad, quiet music corresponding to that emotion can be selected. Furthermore, if the character is happy, bright music corresponding to that emotion can be selected. In this way, music corresponding to the character's emotion can be selected to enhance the atmosphere of the animation.

[0090] The animation generation system can further use the emotion estimation function to generate effects based on the character's emotion. For example, if the character is angry, a fire effect corresponding to that emotion can be generated. If the character is sad, a tear effect corresponding to that emotion can be generated. Furthermore, if the character is happy, a light effect corresponding to that emotion can be generated. This allows for the generation of effects corresponding to the character's emotion and enhances the visual expression.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The original drawing analysis unit analyzes the original drawing. For example, it analyzes the character's movements, changes in the background, the character's facial expressions and posture, and the influence of background lighting. Step 2: The in-between generation unit generates in-between video cuts based on the information analyzed by the original image analysis unit. For example, it generates in-between video cuts that continuously change the character's movement, or in-between video cuts that take into account changes in the background and the effects of light. Step 3: The adjustment unit adjusts the in-between video cuts generated by the in-between generation unit, for example, adjusting the character movements, background changes, and lighting effects so that they appear natural. Step 4: The final generation section generates the final animation using the in-between video cuts adjusted by the adjustment section. For example, the section checks whether the character movements, background changes, and lighting effects are smooth, and corrects any unnatural parts.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an original image analysis unit that analyzes the original image; an in-between generation unit that generates in-between video cuts based on the information analyzed by the original image analysis unit; an adjustment unit that adjusts the in-between video cuts generated by the in-between generation unit; a final generation unit that generates a final animation using the intermediate video cuts adjusted by the adjustment unit. A system characterized by:

2. The original image analysis unit The emotion of the character in the original drawing is estimated, and the movement of the character is analyzed based on the emotion.

2. The system of claim 1.

3. The original image analysis unit Analyze the background information of the original image and perform an analysis taking into account changes in the background and the effects of the light.

2. The system of claim 1.

4. The original image analysis unit Detailed analysis of the movements of characters' clothing and accessories allows for more realistic movements to be reproduced.

2. The system of claim 1.

5. The original image analysis unit When inputting the original image, the 3D model is used in combination to perform a hybrid analysis of the 2D and 3D.

2. The system of claim 1.

Images