Animation demonstration device based on digital media

Abstract

The invention relates to the technical field of digital media, and discloses an animation demonstration device based on digital media, and the device comprises a gesture recognition module which is used for capturing a user gesture, and recognizing a gesture instruction based on multi-modal data fusion and a deep learning model; the intelligent content generation module is used for automatically generating corresponding animation content based on the keywords or descriptions input by the user; the animation engine module is used for rendering and presenting the animation content in real time; and the system control module is used for coordinating the operation of the modules, allocating resources and executing task scheduling so as to ensure the fluency and stability of the device. The gesture recognition module is adopted, the image and depth information of the hand of the user are collected through the camera and the infrared sensor, and the system can stably capture the gesture of the user by combining skin color segmentation, edge detection and a multi-modal data fusion technology.

Description

Technical Field

[0001] The invention relates to the technical field of digital media, in particular to an animation demonstration device based on digital media. Background Art

[0002] With the rapid development of digital media technology, animation demonstration devices are increasingly used in education, training, display and other fields. Such devices can not only display information vividly, but also improve the efficiency of learning and communication through real-time interaction with users. Especially in public places and educational environments, users' demand for contactless and intuitive interaction methods is increasing, which makes the application of advanced technologies such as gesture recognition possible. Using gestures to control animation content can provide users with a more natural and convenient operation experience and promote the improvement of interactive effects.

[0003] There are many gesture recognition-based animation demonstration devices on the market. These devices are usually equipped with cameras and sensors to control the playback of animation content by capturing user gestures. However, the existing technology still has shortcomings in terms of gesture recognition accuracy, intelligent content generation, and smooth user interaction. Some systems rely on preset templates, which limits the user's creative expression; at the same time, the stability of gesture recognition is not satisfactory under different environmental conditions, which affects the overall user experience.

[0004] The main problem with the existing technology is the lack of efficient and intelligent content generation and gesture recognition systems, which leads to poor user experience during use. Especially in public places or teaching scenarios, users want to achieve instant control of animation content through natural gestures, but due to the lack of accuracy in gesture recognition, it often leads to operation delays or recognition errors. This problem directly affects the user's interactive experience and reduces the applicability of the system. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an animation demonstration device based on digital media, which solves the problem that the main problem of the prior art is the lack of an efficient and intelligent content generation and gesture recognition system, resulting in a poor user experience during use.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an animation demonstration device based on digital media, the device comprising:

[0007] Gesture recognition module, used to capture user gestures and recognize gesture commands based on multimodal data fusion and deep learning models;

[0008] Intelligent content generation module, used to automatically generate corresponding animation content based on keywords or descriptions input by users;

[0009] Animation engine module, used for rendering and presenting the animation content in real time;

[0010] The system control module is used to coordinate the operation of the above modules, allocate resources and perform task scheduling to ensure the smoothness and stability of the device.

[0011] Preferably, the gesture recognition module includes:

[0012] A data acquisition unit, used to collect user hand images and depth information through a camera and an infrared sensor;

[0013] A data preprocessing unit, used to perform skin color segmentation and edge detection on the collected images, and extract the hand contour in combination with the depth information;

[0014] The multimodal data fusion unit is used to fuse the visual image and depth information of the user's hand and generate a smooth gesture trajectory.

[0015] Preferably, the gesture recognition module further includes a gesture recognition unit, which performs gesture recognition based on a three-dimensional convolutional neural network and a bidirectional long short-term memory network model to extract and recognize the spatiotemporal characteristics of the user's gesture.

[0016] Preferably, the intelligent content generation module includes:

[0017] A semantic analysis unit, used to perform semantic analysis based on the keywords or descriptions input by the user and extract semantic information of the keywords;

[0018] The content generation unit is used to generate animation content corresponding to the keyword description through a conditional generative adversarial network based on the keyword information of semantic analysis.

[0019] Preferably, the content generation unit further includes an adaptive generation unit, which uses a conditional variational autoencoder to perform real-time adaptive adjustment on the animation content to update the details of the animation content according to the user's operation.

[0020] Preferably, the animation engine module includes:

[0021] A rendering unit, used for performing ray tracing rendering on the generated animation content to generate an animation scene with realistic light and shadow effects;

[0022] The motion blur and anti-aliasing processing unit is used to simulate the motion blur effect in fast-moving animation scenes and use anti-aliasing technology to optimize the edge smoothness of animation content.

[0023] Preferably, the animation engine module further includes an instant feedback unit for monitoring user gesture operations and responding in real time to achieve instant feedback of operations such as playing, pausing, fast forwarding, rotating viewing angle, etc. of the animation content.

[0024] Preferably, the system control module includes:

[0025] Dynamic task scheduling unit, which is used to allocate system resources according to task urgency and resource requirements based on a dynamic priority scheduling algorithm to ensure the real-time performance of gesture recognition and animation rendering;

[0026] The resource management unit is used to dynamically predict the resource load of each module, automatically recycle idle resources, and optimize the utilization efficiency of system resources.

[0027] Preferably, the dynamic task scheduling unit dynamically adjusts resource allocation based on a load prediction algorithm to ensure coordinated operation of multiple modules under high load conditions and achieve reasonable allocation and optimization of resources.

[0028] Preferably, the device further comprises a display unit having a touch function and equipped with a high-definition display screen, for presenting animation content and providing an interactive platform for users and the animation content.

[0029] Working principle: The core components of the device include gesture recognition module, intelligent content generation module, animation engine module and system control module. First, the gesture recognition module uses the camera and infrared sensor to obtain the image and depth information of the user's hand through the data acquisition unit; then, the data preprocessing unit performs skin color segmentation and edge detection on the image, and extracts the hand contour in combination with the depth information to filter out the background noise. Subsequently, the processed data enters the multimodal data fusion unit, which fuses the visual image with the depth information to generate a stable gesture trajectory, thereby ensuring the accuracy and fluency of gesture recognition. Finally, the gesture recognition unit extracts the spatiotemporal characteristics of the gesture based on the three-dimensional convolutional neural network and the bidirectional long short-term memory network model, realizes the accurate recognition of the gesture, converts the gesture action into the corresponding control instructions (such as play, pause, rotate, etc.), and passes these instructions to the animation engine module; in the intelligent content generation module, the semantic parsing unit parses the keywords or descriptions input by the user, extracts the core semantic information from it, and uses it to generate animation scenes that match the content theme. The semantic parsing results are input to the content generation unit, which generates detailed animation content based on the conditional generative adversarial network to ensure that the generated content is highly matched with user needs. In addition, the content generation unit also includes an adaptive generation unit, which dynamically adjusts the details of the animation content, such as the perspective, object appearance, etc., according to the user's real-time operation through the conditional variational autoencoder to achieve personalized display; the animation engine module is responsible for rendering and presenting the generated animation content in real time. Specifically, the rendering unit uses ray tracing technology to generate high-quality light and shadow effects to enhance the realism of the animation; in dynamic scenes, the dynamic blur and anti-aliasing processing unit of the animation engine module simulates the motion blur effect and smoothes the edges to avoid the jagged phenomenon caused by the rapid movement of objects. In order to provide a better user experience, the animation engine module is also equipped with an instant feedback unit to monitor gesture operations in real time and achieve rapid response to operations such as play, pause, fast forward, and perspective rotation according to the user's instructions, ensuring instant interaction between users and content; the system control module, as the core coordinator of the device, uses the dynamic task scheduling unit to manage the execution order of each task, and allocates system resources according to the urgency and resource requirements of the task based on the dynamic priority scheduling algorithm to ensure that real-time tasks are met first. During this process, the resource management unit dynamically monitors the resource requirements of each module and adjusts resource allocation through the load prediction algorithm, and automatically recycles resources when they are idle to optimize resource utilization efficiency and ensure coordinated operation and smoothness of the system under high load conditions. In addition, the system control module is interconnected with the display unit, which is equipped with a high-definition touch screen for presenting animation content and serving as an interactive platform, allowing users to not only control animations through gestures, but also touch operations directly on the display screen to achieve a more natural interactive experience.

[0030] The present invention provides an animation demonstration device based on digital media, which has the following beneficial effects:

[0031] 1. The present invention adopts a gesture recognition module, collects images and depth information of the user's hands through a camera and an infrared sensor, and combines skin color segmentation, edge detection and multimodal data fusion technology. The system can stably capture user gestures and recognize them based on a three-dimensional convolutional neural network and a bidirectional long short-term memory network (BiLSTM) model. This design enables users to control animation content through natural gestures. The operation method is intuitive, convenient and contactless, and is particularly suitable for interactive needs in public places or teaching scenarios.

[0032] 2. The intelligent content generation module of the present invention combines the BERT model and the conditional generative adversarial network (cGAN) to realize the automatic generation of content, and can generate animation content that conforms to a specific theme according to the keywords or descriptions entered by the user, without the need to preset animation templates. The adaptive generation unit is based on the conditional variational autoencoder (CVAE), which can dynamically adjust animation details such as object color, perspective switching, etc. according to the user's real-time operation during the content generation process. This intelligent and personalized content generation method enables the system to generate exclusive animation display content for different users and different subject requirements, greatly improving the applicability and diversity of the display content, and is suitable for flexible application in scenarios such as education, training, and display.

[0033] 3. The present invention uses ray tracing rendering technology to generate realistic light and shadow effects through the animation engine module, so that the animation content has a higher sense of immersion. At the same time, the dynamic blur and anti-aliasing processing unit can effectively simulate the motion blur effect when the animation moves quickly, and ensure the smooth transition of the image edge through anti-aliasing technology to eliminate the jagged phenomenon. This design achieves the smoothness and clarity of the animation in high-speed changing scenes, so that users have a better visual experience when watching the content. It is particularly suitable for displaying physical phenomena or scene restoration that require a high sense of reality, and enhances the expressiveness of animation demonstrations.

[0034] 4. The present invention configures an instant feedback unit through the animation engine module, monitors the user's gesture changes in real time through the event monitoring mechanism, and responds immediately. The user's gesture operation can be instantly transmitted to the animation engine module to achieve instant control of the content, such as play, pause, fast forward, rotate the view, etc., to ensure that every operation of the user can get timely feedback. This fast response design enhances the user's operating experience, keeps the system's response speed consistent with the user's expectations, and thus improves the interactive fluency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the main framework diagram of the present invention;

[0036] Figure 2 It is a flow chart of the user input module of the present invention;

[0037] Figure 3 The flow chart of the module for generating control instructions of the present invention;

[0038] Figure 4 It is a flow chart of the semantic control instruction module of the present invention;

[0039] Figure 5 This is a flow chart of the multimodal data fusion unit of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Please see attached Figure 1 - Attachment Figure 5 The embodiment of the present invention provides an animation demonstration device based on digital media, the device comprising:

[0042] Gesture recognition module, used to capture user gestures and recognize gesture commands based on multimodal data fusion and deep learning models;

[0043] Intelligent content generation module, used to automatically generate corresponding animation content based on keywords or descriptions input by users;

[0044] Animation engine module, used for real-time rendering and presentation of animation content;

[0045] The system control module is used to coordinate the operation of the above modules, allocate resources and perform task scheduling to ensure the smoothness and stability of the device.

[0046] In one embodiment, the user inputs operation instructions through gestures, and the gesture recognition module transmits the gesture signal to the system control module. The system control module coordinates the operation of the intelligent content generation module and the animation engine module, generates personalized animation content according to the gestures and presents it in real time. The modules work together under the unified scheduling of the system control module to complete content display and interaction. The device adopts modular design, which is not only convenient for expanding the functions of each module, but also can quickly generate customized content according to user needs. It has the advantages of convenient operation, sensitive response, and rich display effects.

[0047] Furthermore, the gesture recognition module: This module is used to capture the user's gesture movements and obtain the image and depth information of the user's hand through the camera and infrared sensor. Through multimodal data fusion and deep learning models, the system can accurately recognize the user's gesture instructions. Specifically, the multimodal data fusion unit of the gesture recognition module can comprehensively analyze visual and depth data to generate a stable and smooth gesture trajectory, ensuring that a high recognition accuracy can be maintained in multi-angle and dynamic scenes.

[0048] The gesture recognition module enables users to control animation content through intuitive gestures. The operation is natural and contactless, which not only improves the user's operating experience, but also provides a convenient interaction method for intelligent teaching scenarios.

[0049] Intelligent content generation module: This module automatically generates animation content that matches the keywords by parsing the keywords or descriptions input by the user. Specifically, the intelligent content generation module first uses the semantic parsing unit to analyze the semantic information of the user input content, and matches the corresponding scene template in the content generation unit according to the parsing results. The content generation unit further generates detailed animation content through the conditional generative adversarial network (cGAN).

[0050] The intelligent content generation module enables the device to flexibly generate customized content according to different user needs, reducing dependence on preset scenes, greatly improving the adaptability and display effect of the device, and is particularly suitable for scenarios such as teaching and display that require personalized content.

[0051] This module is used to render and present the generated animation content in real time. Through ray tracing rendering technology, the system can simulate real light and shadow effects, making the animation scene more immersive. The animation engine module is further equipped with a dynamic blur and anti-aliasing processing unit, which can automatically generate dynamic blur effects in fast-moving scenes, and apply anti-aliasing technology to smooth the edges to avoid jagged images.

[0052] The animation engine module significantly improves the visual effects of animations, ensuring the smoothness and realism of animations. Especially when displaying fast-moving content, motion blur and anti-aliasing processing can provide clear visual feedback, further improving the user experience of the device.

[0053] System control module: This module is used to coordinate the work of various modules such as gesture recognition, content generation and animation rendering, and dynamically allocate system resources according to task requirements. Through the dynamic task scheduling unit, the system can prioritize tasks according to the urgency and resource requirements, ensuring that tasks with high real-time requirements such as gesture recognition and animation rendering are given priority. At the same time, the system control module is equipped with a resource management unit, which estimates the resource requirements of each module through a load prediction algorithm, dynamically allocates resources and recycles idle resources to avoid wasting system resources.

[0054] The system control module enables the device to maintain smooth operation under high load conditions, ensuring the user's interactive experience. In addition, the dynamic resource allocation and recycling mechanism also effectively improves the stability of the system, making it suitable for long-term continuous use scenarios.

[0055] The gesture recognition module includes:

[0056] A data acquisition unit, used to collect user hand images and depth information through a camera and an infrared sensor;

[0057] A data preprocessing unit, used to perform skin color segmentation and edge detection on the collected images, and extract the hand contour in combination with the depth information;

[0058] The multimodal data fusion unit is used to fuse the visual image and depth information of the user's hand and generate a smooth gesture trajectory.

[0059] In one embodiment, the gesture recognition module first collects the image and depth information of the user's hand, and the data preprocessing unit performs skin color segmentation and edge detection on the image to extract the hand contour. The multimodal data fusion unit combines the visual image of the hand with the depth information to generate a smooth gesture trajectory, providing high-quality input data for subsequent gesture recognition; through the multimodal fusion of visual images and depth information, the module can more accurately capture the spatial position information of the user's hand, improve the accuracy and stability of gesture recognition, and enhance the user's operating experience.

[0060] Furthermore, the data acquisition unit: The data acquisition unit collects the RGB image of the user's hand through the camera, and collects the depth information of the user's hand through the infrared sensor. The collected data provides a basic information source for subsequent gesture recognition.

[0061] Using dual sensors to collect data helps ensure the accuracy and stability of gesture recognition, and can obtain stable hand data even in complex lighting conditions.

[0062] Data preprocessing unit: In the data preprocessing unit, the system first performs skin color segmentation and edge detection to separate the hand area from the background. Through the skin color segmentation algorithm, the system can effectively remove background noise and ensure the clarity of the gesture outline.

[0063] Data preprocessing helps to improve the accuracy of gesture recognition and provides good quality image input for subsequent multimodal data fusion.

[0064] Multimodal data fusion unit: Based on the Kalman filter algorithm, the multimodal data fusion unit fuses visual data and depth data to generate a stable gesture trajectory. By fusing visual and depth information, the system can maintain high recognition accuracy in complex environments and can handle gesture operations at different angles and distances.

[0065] Multimodal fusion can effectively enhance the robustness of gesture recognition, allowing the system to accurately track user gestures even in poor lighting conditions.

[0066] The gesture recognition module further includes a gesture recognition unit, which performs gesture recognition based on a three-dimensional convolutional neural network and a bidirectional long short-term memory network model to extract and recognize the spatiotemporal features of the user's gestures.

[0067] In one embodiment, the gesture recognition unit extracts the spatial features of the hand image through 3DCNN, and combines BiLSTM to capture the time series characteristics of the gesture action to achieve accurate recognition of complex gestures. For example, when the user performs a "rotation" gesture, 3DCNN extracts the spatial contour features, and BiLSTM tracks the dynamic changes of the gesture to generate a "rotation" command signal.

[0068] This combined model enables the system to recognize complex dynamic gestures, improve recognition accuracy and speed, support a variety of gesture operations, and provide users with a highly interactive experience.

[0069] 3DCNN extracts spatial features: 3DCNN extracts spatial features of hand images through three-dimensional convolution operations. The convolution operation formula is as follows:

[0070]

[0071] Where W is the three-dimensional convolution kernel, X is the input image feature, and b is the bias. The spatial convolution of 3D CNN can effectively extract the contour and shape features of gestures in three-dimensional space.

[0072] Bi LSTM captures time series features: Bi LSTM processes time series features through a bidirectional hidden layer. The forward and backward hidden states are calculated as follows:

[0073]

[0074] Bidirectional processing enables the system to recognize complex dynamic gestures such as "rotation" and "zooming" and generate corresponding command signals; through the spatiotemporal combination model of 3D CNN and BiLSTM, the device achieves high-precision recognition of complex dynamic gestures, providing users with a smooth interactive experience.

[0075] The intelligent content generation module includes:

[0076] A semantic analysis unit, used to perform semantic analysis based on the keywords or descriptions input by the user and extract semantic information of the keywords;

[0077] The content generation unit is used to generate animation content corresponding to the keyword description through a conditional generative adversarial network based on the keyword information of semantic analysis.

[0078] In one embodiment, the semantic parsing unit: The semantic parsing unit performs semantic parsing on the keywords or descriptive text input by the user based on the BERT model to extract the core semantic information of the text. The system calculates the dependency between words through the self-attention mechanism of BERT, thereby accurately extracting the theme of the input content.

[0079] The semantic parsing unit enables the system to accurately understand the user's input intention, achieve more intelligent content generation, and meet the personalized needs of different users.

[0080] Content generation unit: The content generation unit uses conditional generative adversarial networks (cGAN) to generate corresponding animation content under the parsed theme conditions. The content generation unit further includes an adaptive generation unit, which adaptively adjusts the generated content through conditional variational autoencoders (CVAE) so that the animation details can be updated with user operations.

[0081] Through conditional generative adversarial networks and adaptive generation mechanisms, the system can dynamically generate animation content for different scenes based on user input and adjust details in real time during user operations, enhancing the intelligence and dynamic interaction effects of the system.

[0082] The content generation unit further includes an adaptive generation unit, which uses a conditional variational autoencoder to perform real-time adaptive adjustment on the animation content to update the details of the animation content according to the user's operation.

[0083] In one embodiment, the rendering unit renders the animation content based on a ray tracing algorithm to generate a realistic light and shadow effect. The ray tracing algorithm simulates the propagation path of light in the scene, making the animation content realistic.

[0084] Ray tracing rendering provides realistic light and shadow effects for animations, making users more immersed, and is particularly suitable for teaching demonstrations and immersive display scenes.

[0085] Motion blur and anti-aliasing processing unit: In fast-moving animation scenes, the motion blur processing unit automatically generates a motion blur effect, while the anti-aliasing processing unit smoothes the edges through multi-sampling to avoid jagged edges when the animation content moves.

[0086] Motion blur and anti-aliasing processing ensure the smoothness and visual clarity of animations when they move and change quickly, improving user viewing comfort.

[0087] Instant feedback unit: The instant feedback unit monitors the user's gesture operations in real time, and responds to the animation content instantly by playing, pausing, rotating, etc. according to the user's instructions; the instant feedback unit can respond to user instructions quickly, so that the animation content changes synchronously with the user's operations, enhancing the user's sense of participation and control.

[0088] The animation engine module includes:

[0089] A rendering unit, used for performing ray tracing rendering on the generated animation content to generate an animation scene with realistic light and shadow effects;

[0090] The motion blur and anti-aliasing processing unit is used to simulate the motion blur effect in fast-moving animation scenes and use anti-aliasing technology to optimize the edge smoothness of animation content.

[0091] In one embodiment, a dynamic task scheduling unit: The dynamic task scheduling unit is based on a dynamic priority scheduling algorithm, and allocates system resources according to the urgency of the task and resource requirements, ensuring that real-time tasks such as gesture recognition and animation rendering are executed first to ensure the smoothness of the system; The dynamic task scheduling unit improves the real-time response capability of the system, allowing users to experience smoothness and no delay during operation, which is suitable for the needs of multi-task parallel processing in complex teaching scenarios.

[0092] Resource Management Unit: The resource management unit dynamically monitors the resource requirements of each module through a load prediction algorithm, automatically allocates resources, and recycles resources when they are idle to optimize the utilization efficiency of system resources. The resource management unit ensures the efficient operation of the system and can still provide stable performance when resources are limited. It is suitable for application scenarios that require long-term operation.

[0093] The animation engine module further includes an instant feedback unit for monitoring user gesture operations and responding in real time to achieve instant feedback of operations such as playing, pausing, fast forwarding, rotating viewing angle, etc. of the animation content.

[0094] In one embodiment, the instant feedback unit is implemented as follows: the instant feedback unit monitors the user's gesture operation in real time through an event monitoring mechanism. Whenever a user's gesture change is detected, such as a gesture indicating "play" or "pause", the feedback unit immediately passes the instruction to the system control module, and the control module then sends the instruction to the animation engine module to achieve real-time control of the animation content. To ensure the response speed, the instant feedback unit sets a low-latency response target of less than 16ms to ensure the synchronization of user operations and animation changes.

[0095] Working principle of instant feedback: The instant feedback unit analyzes the dynamic changes of gestures in real time through the continuous data stream of sensors and gesture recognition modules. The built-in buffer mechanism of the feedback unit can be executed at the moment of receiving the command to ensure that complex gesture changes can also be quickly fed back to the animation content; the instant feedback unit can quickly respond to the user's gesture operation, and the animation content can be adjusted synchronously under the user's gesture command, making the user experience more smooth and natural. It is suitable for interactive educational scenarios that require real-time feedback, and improves the real-time and interactivity of the device.

[0096] The system control module includes:

[0097] Dynamic task scheduling unit, which is used to allocate system resources according to task urgency and resource requirements based on a dynamic priority scheduling algorithm to ensure the real-time performance of gesture recognition and animation rendering;

[0098] The resource management unit is used to dynamically predict the resource load of each module, automatically recycle idle resources, and optimize the utilization efficiency of system resources.

[0099] In one embodiment, the implementation of the dynamic task scheduling unit: the dynamic task scheduling unit assigns priority to each task based on a dynamic priority scheduling algorithm, and determines the order of task execution according to the task urgency and resource requirements. For example, for real-time tasks such as gesture recognition and animation rendering, the scheduling unit will give them a higher priority so that the system can prioritize the stable operation of these tasks; the unit monitors and analyzes system tasks, calculates the urgency and required resources of the current task in real time, and dynamically adjusts the priority for each task. The scheduling unit adopts a multi-level queue strategy to ensure that high-priority tasks can still run first when resources are limited. Whenever the user issues a gesture control command, the scheduling unit will respond immediately and schedule the corresponding resources to ensure real-time response of the task.

[0100] The dynamic task scheduling unit can effectively ensure the smooth operation of the device under multi-task load, so that key tasks such as gesture recognition, content generation and animation rendering can be executed in a timely manner, improving the overall response efficiency of the system.

[0101] The dynamic task scheduling unit dynamically adjusts resource allocation based on the load prediction algorithm to ensure the coordinated operation of multiple modules under high load conditions and achieve reasonable allocation and optimization of resources.

[0102] In one embodiment, the implementation of the load prediction algorithm: the load prediction algorithm is used to estimate the resource requirements of each module and dynamically adjust the resource allocation according to the predicted load conditions. In the specific implementation, the load prediction algorithm adopts an exponential smoothing model to estimate the load requirements at the next moment by sampling the historical load data of each task; the system monitors the current load of the gesture recognition module, the content generation module, and the animation engine module in real time. Through the load prediction algorithm, the system can pre-allocate resources before the load peak to avoid delays caused by insufficient resources under high load conditions. For example, when it is detected that the load of the gesture recognition module increases, the system will give priority to allocating resources to the gesture recognition module to ensure the smoothness of gesture control. The load prediction algorithm can predict the resource usage of the system in advance, realize the reasonable allocation and optimization of resources, and improve the overall stability of the system under high load conditions of multiple modules. It is suitable for long-term teaching and demonstration scenarios.

[0103] The device further includes a display unit having a touch function and equipped with a high-definition display screen, which is used to present the animation content and provide an interactive platform for the user and the animation content.

[0104] In one embodiment, the display unit is implemented as follows: the display unit uses a high-definition 4K display screen with a touch function to display the animation content generated in real time. The display unit is integrated with the gesture recognition module and the animation engine module, so that the user can not only control the animation content through gestures, but also perform touch operations directly on the display screen, such as zooming in, zooming out and rotating the viewing angle. When the user performs gesture or touch operations, the display unit will update the animation content in real time. The touch function is interconnected with the system control module. The user's touch operation is converted into an animation control signal after system processing, which is executed and presented in real time by the animation engine module, so that the user can obtain a more intuitive control experience. The display unit equipped with a high-definition display screen and a touch function can clearly present the animation content. The touch operation further enhances the interactivity of the device, so that the user can obtain a more intuitive interactive experience, which is suitable for educational demonstrations and immersive display applications.

[0105] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An animation demonstration device based on digital media, characterized in that: The device comprises: Gesture recognition module, used to capture user gestures and recognize gesture commands based on multimodal data fusion and deep learning models; Intelligent content generation module, used to automatically generate corresponding animation content based on keywords or descriptions input by users; Animation engine module, used for rendering and presenting the animation content in real time; The system control module is used to coordinate the operation of the above modules, allocate resources and perform task scheduling to ensure the smoothness and stability of the device.

2. The digital media-based animation demonstration device according to claim 1, characterized in that: The gesture recognition module comprises: A data acquisition unit, used to collect user hand images and depth information through a camera and an infrared sensor; A data preprocessing unit, used to perform skin color segmentation and edge detection on the collected images, and extract the hand contour in combination with the depth information; The multimodal data fusion unit is used to fuse the visual image and depth information of the user's hand and generate a smooth gesture trajectory.

3. The digital media-based animation demonstration device according to claim 1, characterized in that: The gesture recognition module further includes a gesture recognition unit, which performs gesture recognition based on a three-dimensional convolutional neural network and a bidirectional long short-term memory network model to extract and recognize the spatiotemporal features of the user's gesture.

4. The digital media-based animation demonstration device according to claim 1, characterized in that: The intelligent content generation module includes: A semantic analysis unit, used to perform semantic analysis based on the keywords or descriptions input by the user and extract semantic information of the keywords; The content generation unit is used to generate animation content corresponding to the keyword description through a conditional generative adversarial network based on the keyword information of semantic analysis.

5. The digital media-based animation demonstration device according to claim 1, characterized in that: The content generation unit further includes an adaptive generation unit, which uses a conditional variational autoencoder to perform real-time adaptive adjustment on the animation content to update the details of the animation content according to the user's operation.

6. The digital media-based animation demonstration device according to claim 1, characterized in that: The animation engine module includes: A rendering unit, used for performing ray tracing rendering on the generated animation content to generate an animation scene with realistic light and shadow effects; The motion blur and anti-aliasing processing unit is used to simulate the motion blur effect in fast-moving animation scenes and use anti-aliasing technology to optimize the edge smoothness of animation content.

7. The digital media-based animation demonstration device according to claim 1, characterized in that: The animation engine module further includes an instant feedback unit for monitoring user gesture operations and responding in real time to achieve instant feedback of operations such as playing, pausing, fast forwarding, rotating viewing angle, etc. of the animation content.

8. The digital media-based animation demonstration device according to claim 1, characterized in that: The system control module comprises: Dynamic task scheduling unit, which is used to allocate system resources according to task urgency and resource requirements based on a dynamic priority scheduling algorithm to ensure the real-time performance of gesture recognition and animation rendering; The resource management unit is used to dynamically predict the resource load of each module, automatically recycle idle resources, and optimize the utilization efficiency of system resources.

9. The digital media-based animation demonstration device according to claim 8, characterized in that: The dynamic task scheduling unit dynamically adjusts resource allocation based on a load prediction algorithm to ensure coordinated operation of multiple modules under high load conditions and achieve reasonable allocation and optimization of resources.

10. The digital media-based animation demonstration device according to claim 1, characterized in that: The device further includes a display unit, which has a touch function and is equipped with a high-definition display screen, and is used to present the animation content and provide an interactive platform for the user and the animation content.

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