1842 results about "Human-body model" patented technology

The invention belongs to the technical field of knowledge visualization, and discloses a traditional Chinese medicine acupuncture knowledge visual display system based on a knowledge graph. Comprising the steps that a traditional Chinese medicine ancient book data acquisition unit, a clinical case data acquisition unit and an ultrasonic image data acquisition unit acquire data and integrate the data into traditional Chinese medicine acupuncture data, and the traditional Chinese medicine acupuncture data is preprocessed to obtain perfect traditional Chinese medicine acupuncture data; constructing a knowledge graph based on the perfect traditional Chinese medicine acupuncture data; constructing an optimization network model to optimize the knowledge graph to obtain a high-quality knowledge graph; constructing an incremental learning framework, updating the high-quality knowledge graph based on the incremental learning framework, and outputting a real-time updated knowledge graph; constructing an interactive three-dimensional human body model based on the real-time updated knowledge graph; based on the real-time updated knowledge graph and the interactive three-dimensional human body model, outputting an executable medical strategy for the application case, and sending the executable medical strategy to the student terminal; and the teaching quality is improved while knowledge visualization is realized.

The invention discloses a virtual fitting method and device, a storage medium and electronic equipment, and is applied to the technical field of computer image processing, and the method comprises the steps: processing a target video, and obtaining the human body posture data of each video frame in the target video; constructing a 3D human body model of each video frame by using the human body posture data of each video frame; for the 3D human body model of each video frame, acquiring clothing rendering information of the 3D human body model, and rendering virtual clothing on the 3D human body model based on a dynamic fitting algorithm and the clothing rendering information to obtain a virtual rendering video frame of the video frame; and generating an AR fitting video based on each virtual rendering video frame, and displaying the AR fitting video to the user. Therefore, the selected costume can be tried on in the AR form, the fitting effect of the costume can be displayed for the user, the problem that the size is improper due to the fact that the costume purchased online cannot be tried on is solved, the probability of refunding and changing goods is reduced, and good shopping experience is provided for the user.

The invention provides a virtual fitting method based on a color three-dimensional model, and the method comprises the steps: obtaining the body point cloud data of a user through a three-dimensional scanning device, constructing a color three-dimensional human body model, and setting skeleton binding parameters; dividing a pressure sensitive area based on model surface curvature distribution, and generating a dynamic pressure map; performing cloth physical simulation on the model in a physical engine, and extracting deformation response data of the top points of the clothing grids; real-time motion posture data of a user is collected through an inertial sensor, motion distortion correction is conducted, and a joint motion track is generated. Dynamically comparing the joint movement track with the deformation response data to generate a clothing fitting degree deviation report; and optimizing the clothing pattern parameters and the fabric physical attributes based on the deviation report, and outputting a clothing adaptation proposal. According to the invention, the suitability and comfort of clothes can be improved, and the production efficiency and the product quality are improved.

The invention belongs to the technical field of three-dimensional reconstruction, and discloses a three-dimensional digital human generation method and system capable of voice interaction. According to the invention, brand new speaking audios in different languages are automatically generated according to different languages of the input target text and the sampled human voice audios; the sequential stability and detail reduction capability of three-dimensional human motion are guaranteed by using multi-model joint estimation and a sequential loss function, and facial expression details and hand postures in the image can be accurately estimated. After the high-precision three-dimensional human body model is obtained through estimation, human body action and expression generation is carried out based on voice driving, accurate synchronization of actions and expressions generated through voice is achieved, and facial expression movement and body posture movement, namely a whole-body three-dimensional human body model, conforming to brand-new speaking audio are accurately generated; and finally, rendering the whole-body three-dimensional human body model into a real digital human capable of voice interaction by using a three-dimensional neural rendering model. According to the invention, the realization of single person picture input, high-precision three-dimensional digital person generation and voice interaction is facilitated.

In an embodiment, a method for human motion generation with open vocabulary scene-and-text context is provided. The method involves receiving an input that includes a 3D point cloud of a scene containing a goal object with a natural language instruction related to the goal object. A text tokenizer is applied to the text to obtain tokenized text, and a text encoder from a pre-trained vision-language model generates text features. First scene features are generated by applying a pre-trained U-Net scene encoder to the 3D point cloud, which are down sampled to obtain second scene features. A conditional latent is obtained by fusing the second scene features with the text features. A conditional motion generator predicts motion parameters for a parametric human body model over a specific time duration. Finally, 3D human meshes for multiple motion frames are obtained based on the motion parameters and the parametric human body model.

The invention discloses an intelligent scene type CPR visual training system based on an MR technology, which belongs to the technical field of non-special measurement for specific variables and comprises a training request sensing module, an intelligent scene automatic generation module, a sensing data acquisition module, a visual display and voice prompt module and a network performance evaluation management and control module. According to the method, the performance parameters of the sensing source nodes built in the simulation human body model are acquired and analyzed in real time, so that the data feedback precision in the training process is improved, and the data feedback precision in the training process is further improved; the problem of poor CPR visual training experience caused by lack of real-time performance monitoring and feedback mechanisms in the prior art is effectively solved.

The invention relates to an editable human body reconstruction method and system based on three-dimensional Gaussian splashing, and the method converts a dynamic human body reconstruction problem into a reconstruction problem of a static template human body in a standard space, achieves the respective decoupling of the shape and the appearance through a mode of constructing the mapping of a three-dimensional Gaussian model and a parameterized human body model, and achieves the reconstruction of the human body. Human body reconstruction in an editable form is achieved, and the editable form comprises the steps that geometric editing is achieved by changing a body shape coefficient and a pose coefficient in the parameterized human body model; and replacing the vertex mapped three-dimensional Gaussian point cloud in the editable three-dimensional Gaussian model to realize texture editing. Compared with the prior art, the method solves the problem that personalized editing cannot be carried out on the human body model based on three-dimensional Gaussian splashing in the prior art.

The invention discloses a 3D Gaussian-based dressing human body geometry and texture parameterization reconstruction method. The method comprises the steps of generating human body and clothing grids of each frame of an RGB video based on a human body parameterization model SMPL and a clothing parameterization model TailorNet; generating a subdivision grid based on the human body and clothing grid, and generating an initial 3D Gaussian ball by taking the subdivision grid as a reference; training a Gaussian estimation network, and rendering by using a linear skin and 3D Gaussian splashing method; a motion sequence is given, and a dressed human body under the motion sequence is rendered by using a Gaussian estimation network. The invention provides the dressing human body reconstruction method which is more fit with the clothing and the human body model, the dressing human body rendering result under the given motion sequence can be output after the network training is completed, and the real and natural clothing wrinkle geometry and texture can be reconstructed.

The invention relates to a single-image dressed human body reconstruction method based on posture guide diffusion and view angle consistency constraint, and belongs to the field of computer vision and graphic images. The method comprises the following steps: firstly, carrying out multi-view image generation on a single image in an input dressed human body data set by adopting posture guide diffusion to obtain a two-dimensional diffusion image of an invisible view angle; secondly, establishing a visual angle consistency constraint module, taking the input image and the diffusion image as bidirectional visual angle features, performing implicit geometric guidance in the normal direction of the SMPLX human body template, and respectively extracting three-dimensional space features corresponding to cross visual angles; and finally, fusing voxelization features and three-dimensional space features in the SMPLX human body template to carry out human body implicit reconstruction to obtain a dressed human body model. According to the method, the problems of low reconstruction quality, clothing detail missing and the like caused by insufficient input information of a single image can be effectively solved, and high-quality three-dimensional reconstruction of a dressed human body is realized.

The invention relates to the technical field of computer graphics and artificial intelligence, and discloses a meta-universe digital human generation method and system based on deep learning, and the method comprises the steps: constructing a neural radiation field human body model through multi-view video data, and learning a user behavior sequence mode; collecting sparse sensor data of the head and the hands of the user and generating a preliminary whole-body skeleton posture; calculating contact force feedback when the digital human interacts with the virtual environment through a differentiable physical engine; utilizing a reinforcement learning algorithm to optimize postures to meet physical constraints; performing multi-objective optimization and quality evaluation on the generated attitude; according to the method, the digital human postures which have personalized features and can naturally interact with the environment can be generated by using the consumer-level equipment, and the meta-universe interaction experience is improved.

The invention discloses a garment generation model training method, a digital human garment generation method and hardware. The method comprises the following steps: acquiring a three-dimensional human body model of a three-dimensional digital human to be dressed; obtaining first section data, and processing the first section data based on the three-dimensional human body model to obtain a three-dimensional grid model of the initial garment and second section data corresponding to the three-dimensional grid model; acquiring clothing image data including clothing texture information, and generating target image data including target clothing texture information based on the clothing image data and the second section data; the target image data is used for generating three-dimensional clothes data for dressing the three-dimensional digital human with the three-dimensional grid model; and obtaining a description text which corresponds to the target image data and is used for describing the clothing, and training the clothing generation model based on the target image data and the corresponding description text to obtain a trained clothing generation model.

The invention relates to the technical field of safety performance testing, and discloses an anti-falling waistcoat protection performance testing method and system. According to the method, a bionic human body model is driven through a multi-degree-of-freedom impact simulation device, a standard test dummy wearing an anti-falling waistcoat is impacted in multiple preset postures including the combination of a pitch angle and a roll angle, and a real falling scene is simulated; a flexible piezoresistive sensor array embedded into the inner layer of the waistcoat is used for collecting dynamic pressure distribution data in real time, and an air bag unfolding time sequence image is synchronously recorded; calculating a pressure dispersion rate based on the pressure data, determining protection response delay in combination with image analysis, and generating an attitude adaptability evaluation index through a multi-attitude test result; and finally, executing a continuous inflation and deflation cycle test to evaluate the sealing performance and the sensor stability. According to the method, the dynamic protection performance, the airbag response efficiency and the long-term use reliability of the anti-falling waistcoat in a complex impact posture can be comprehensively detected, and the consistency of a test result and an actual protection effect is improved.

The invention provides a 3D modeling-assisted garment pattern making method and system, and relates to the field of garment design, and the method comprises the steps: building a fabric database according to the fabric data on the current market, and obtaining an initial 3D garment model of a garment; fitting simulation is conducted on the obtained initial 3D garment model on the human body model, parameters of the initial 3D garment model are adjusted according to the simulation result and the fabric data, and an optimal 3D garment model is obtained; splitting the optimal 3D garment model to obtain a corresponding cut-part model and a sewing sequence; cloth is cut according to the cut-part model, solid cut-parts are obtained, the solid cut-parts are sewn according to the sewing sequence, manufacturing and adjusting of the clothes are completed, the pattern is confirmed, and therefore pattern making of the clothes is completed. According to the 3D modeling assisted clothing pattern making method and system, high precision, high efficiency and low cost of clothing pattern making are achieved, and meanwhile the design freedom degree and the wearing comfort degree are improved.

The invention relates to the field of medical education and training. The invention discloses a virtual reality-based operation training system and method, and the system comprises a data collection module which is used for collecting operation related data, a simulation training module which is connected to a data processing and modeling module and is used for the operation simulation training of students, and an evaluation module which is connected to the simulation training module and the data processing and modeling module and is used for the operation simulation training of students. The teaching assisting module is connected to the simulation training module and the evaluation module, and the system management module is used for managing the operation training system. According to the operation training system and method based on virtual reality, various data of an operation can be collected through the data collection module comprising a plurality of sub-modules, the operation force of an operation instrument, the pressure of an operation simulation scene and the operation pressure distribution data of a human body model can also be collected, and basic information is provided for subsequent training and analysis. The simulation training module provides a plurality of operation scenes and training modes. The evaluation module performs quantitative evaluation on multiple aspects of the trainees.

The invention provides a three-dimensional digital human reconstruction method, and the method comprises the steps: estimating a parameterized human body model corresponding to an input single human body reference image based on the input single human body reference image, and obtaining a multi-view human body semantic segmentation image of the parameterized human body model according to a plurality of preset camera poses and rendering parameters; taking the multi-view human body semantic segmentation image as a condition signal, taking the single human body reference image as input, and generating a multi-view human body color image based on a pre-trained first video diffusion model; taking the multi-view human body color image as a condition signal, taking a human body normal image extracted from a single human body reference image as input, and generating a multi-view human body normal image based on a pre-trained second video diffusion model; and performing 3D Gaussian splashing based on the multi-view human body color image and the multi-view human body normal image so as to reconstruct a three-dimensional digital human body corresponding to the single human body reference image.

The invention relates to the technical field of smart home, and discloses a virtual environment image home control method and system under digital twinning, and the method comprises the steps: building a digital twinning body through the physical data of a home scene, and building a user 3D human body model based on the human body scattered wave data, so as to generate a virtual mirror image; creating a communication channel between the virtual mirror image and the home equipment so as to construct a control interface of the virtual mirror image to the home equipment; based on the current behavior intention, setting a personalized control instruction of the virtual mirror image to the home equipment; according to the time sequence image and the behavior tracking network, setting a real-time synchronization mechanism of the virtual mirror image and the home scene, and constructing data binding of the home device and the virtual mirror image; and in combination with the virtual mirror image, the control instruction, the scene synchronization mechanism and the data security interaction system, executing the control instruction of the home equipment. According to the invention, one-to-one correspondence between the pictures displayed by the digital twinning and the real scene can be realized, so that a user can accurately control the actual state of the supervised object.

The invention relates to the technical field of computer vision, and discloses a sparse view-based three-dimensional image reconstruction method and device and computer equipment, and the method comprises the steps: obtaining a multi-view image of a target human body wearing clothes; fitting a three-dimensional human body template of the target human body; extracting a multi-view clothing image; according to the multi-view garment image and the three-dimensional human body template, generating an initial Gaussian point cloud of the garment; constructing a boundary range for the initial Gaussian point cloud, and removing free Gaussian points outside the boundary range; constructing a loss function of two-dimensional Gaussian sputtering, performing multiple optimization iteration training on the initial Gaussian point cloud, and continuously removing free Gaussian points outside the boundary range in the optimization iteration training process to obtain a reconstructed garment model; generating a three-dimensional human body model with clothes according to the three-dimensional human body template and the reconstructed clothes model; according to the method, the quality and availability of three-dimensional human body model reconstruction are improved.

The invention relates to a monocular video three-dimensional human body high-quality reconstruction method based on Gaussian splashing and normal perception, and belongs to the field of computer aided design, graphics and computer vision. The method comprises the following steps: firstly, based on a monocular video frame in an input three-dimensional human body data set, guiding posture deformation through a time sequence, and carrying out whole body correlation deformation on a standard space posture to obtain an observation space human body posture; secondly, through normal perception Gaussian optimization, adaptive Gaussian density control and human body normal mapping supervision are carried out on the human body posture in the observation space, and a human body model of Gaussian representation is obtained; then, carrying out human body shadow feature learning on the input human body normal map by adopting an illumination model to obtain human body shadow features; and finally, light and shadow enhancement human body rendering is performed on the Gaussian representation human body model in combination with the human body light and shadow features and human body color information input into the monocular video, a human body rendering effect is output, and the quality and reality sense of three-dimensional human body reconstruction of the monocular video are improved.

The invention relates to the technical field of power plant supervision, and particularly discloses a BIM-based power plant safety supervision method and platform, and the method comprises the steps: obtaining a BIM model of a power plant, and building a three-dimensional power plant model based on the BIM model; operating parameters of equipment are collected, and a risk area with the equipment model as the center is created in the three-dimensional power plant model according to the operating parameters of the equipment; obtaining a position relationship between the human body model and the risk area, and generating prompt information pointing to the human body model according to the position relationship; and acquiring the real-time position of each human body model in a work cycle, and performing work rating on the human body models according to the relationship between the risk area and the real-time position. According to the method, the recognition result of the camera system is counted by means of the BIM model and then displayed, the risk area with a prediction function is introduced in the display process, risk recognition is performed on the staff according to the predicted risk area, the prompt information is generated, and the safety degree of the staff is improved.

The invention relates to the technical field of human body three-dimensional modeling, in particular to a human body measurement parameter set-based three-dimensional body surface shape generation method, which comprises the following steps of: S1, establishing a parameterized body surface model T; s2, determining a target anthropometric parameter group # imgabs0 #; S3, determining a transformation relation F based on the parameterized body surface model T and the target anthropometric parameter group # imgabs1 #; S4, constructing and verifying an optimized physical sign parameter beta *; and S5, substituting the optimized physical sign parameter beta * into any pose parameter theta, and changing the body surface geometric space pose of the optimized physical sign parameter beta *. By means of the method, finite element modeling of the human body in any form can be achieved, a statistical regression or condition generation model from one-dimensional measurement parameters to three-dimensional human body shapes does not need to be established, and various downstream human body modeling tasks oriented to sign diversity are achieved.

The invention discloses an intelligent guiding method based on a 3D virtual garment customization process, and relates to the technical field of garment customization, and the method comprises the steps: obtaining the basic information of a user through a user interaction interface, and generating a 3D virtual human body model matched with the body type of the user through 3D scanning equipment; performing intelligent matching through a machine learning algorithm according to the basic information of the user and the style information in the costume design database, and displaying a matching recommendation result on a user interface in a visual form for the user to select and preview; dynamically adapting the 3D model of the clothes selected by the user to the 3D virtual human body model, and providing an intelligent adjustment option and an intelligent algorithm to generate an adjustment suggestion; according to the method and the device, fitting simulation, comfort evaluation and visual effect evaluation feedback are performed, user design is optimized, and a customization scheme containing a 3D model file and a production drawing is generated according to a design scheme finally confirmed by a user, so that the operation of a virtual garment customization process is simpler, the auxiliary decision-making experience degree is higher, and the customization efficiency is improved.

The invention discloses a point cloud compression method based on multi-dimensional feature fusion, electronic equipment and a medium, and aims to effectively reduce the compression bit rate while maintaining the precision of a space structure. According to the method, space, channel and topology redundant information is fused under a unified framework for the first time, and the method is suitable for efficient point cloud compression of various three-dimensional scenes such as automatic driving and virtual reality. Meanwhile, a local graph convolution Mama module is introduced, and the limitation that a point cloud topological structure is difficult to capture in a traditional method is effectively solved. Then, the octree nodes are divided into two dimensions of space and channel through a space-channel coupling grouping module, so that gradual feature fusion is realized, and the context information prediction precision is improved; experimental results show that the compression efficiency is remarkably improved on a plurality of radar and voxel human body model data sets.

The invention provides a breast endoscopic surgery simulation training system. The breast endoscopic surgery simulation training system comprises a hardware end and a software end, the hardware end comprises a simulation human body model, and the software end is provided with a 3D mesh model corresponding to the simulation human body model; the simulation surgical instrument comprises a simulation electrotome, a simulation endoscope, simulation grasping forceps and a simulation incision protection sleeve, and the software end is provided with an electrotome model, an endoscope model, a grasping forceps model and an incision protection sleeve model which correspond to one another; the pose detection device is used for detecting the current pose of the simulation surgical instrument in real time and sending the current pose to the software end, so that interaction between the simulation surgical instrument and the simulation human body model is mapped to interaction between the instrument model and the 3D mesh model; the software end further comprises a display screen, and the display screen is used for displaying interaction between the instrument model and the 3D mesh model in real time. According to the scheme, breast endoscopic surgery training, teaching and research which are closer to a real surgery scene can be provided, so that the safety and the operation precision in an actual breast endoscopic surgery are improved.

The invention belongs to the technical field of human body model construction, and particularly relates to a multi-sign human body model spine positioning method combined with deep learning, which comprises the following steps: firstly, based on preset human body characteristic parameters, collecting human body spine image data according to a preset standard posture, and carrying out medical image processing on the collected human body spine image data to generate a data set; performing data preprocessing on the data set, then constructing a prediction model based on a full-connection neural network model, training the prediction model according to the preprocessed data set, reserving model parameters with optimal performance as model parameters of the prediction model, and generating a final prediction model; and finally, deploying the final prediction model, generating a spine positioning parameter result by the deployed prediction model according to the input human body sign parameters, and guiding the development of the multi-sign human body model according to the spine positioning parameter result. According to the invention, the problem of low accuracy of human body model spine positioning in the existing grid transformation technology can be solved.

The invention belongs to the technical field of medical training, and particularly relates to a method and a system for constructing a virtual operating room resource library for reappearing an offshore operation scene, and the method comprises the following steps: 1, collecting offshore medical data and scene demands: collecting an offshore environment and medical data, and determining medical care core capability demands; in the application, coupling parameters generated by a sea condition dynamic sensing module drive a mixed reality scene rendering module to present real shake and environment change; meanwhile, parameters are synchronously input to the equipment modeling module, fault response of virtual equipment is triggered, a user operates the virtual equipment through the interaction module and executes an operation on a virtual human body model, and the resource library management module guarantees reusability and updatability of resources in the whole process. Through real-time data linkage among the modules, total-factor dynamic coupling of sea conditions, scenes, equipment and operation objects is finally realized, and the problem of simulation distortion of a traditional system is solved.

The invention relates to a basketball player motion capture analysis method and system based on an industrial camera. The method comprises the following steps: based on a preset three-dimensional human body model, obtaining a three-dimensional human body surface grid sequence and internal skeleton joint kinematics data by combining a pixel-level mask of each multi-view image frame set; detecting a body contact event and a continuous time period based on the Euclidean distance of the three-dimensional human body surface grid sequence; based on the internal skeleton joint kinematics data, calculating a multi-dimensional kinematics feature time sequence; constructing a dynamic causal hypothesis based on the body contact event, checking the dynamic causal hypothesis based on a preset differentiable forward dynamic multi-rigid-body human body model and a multi-dimensional kinematics characteristic time sequence to obtain a loss function value, and judging the hypothesis with the smaller loss function value as a real dynamic causal relationship. And the identity identification information is combined to obtain the identity of the active force generation party and the identity of the passive force bearing party. By adopting the method, a real force exerting main body and a real force bearing main body in confrontation can be accurately identified.

The invention discloses a video-based digital human motion capture and motion generation method and device and a medium, and relates to the technical field of machine vision, and the method comprises the steps: obtaining a video containing human motion; extracting a body area image frame and a hand area image frame corresponding to the figure based on the video; performing human body reconstruction based on the body area image frame and the hand area image frame to obtain a 3D human body model; and acquiring human skeleton information based on the 3D human body model, and generating a skeleton action file based on the human skeleton information. According to the scheme disclosed by the invention, the digital human body motion capture cost is reduced, and the problem that animation needs to be made again due to skeleton adjustment is solved.

The invention relates to the cross technical field of medical education and mixed reality technology, in particular to a traditional Chinese medicine meridian point visualization system based on mixed reality technology, which comprises a data fusion and personalized calibration module, a virtual-real fusion interaction module, an interaction teaching module, an intelligent evaluation module and a meridian qi and blood simulation module. By fusing medical image data, depth sensing data and a traditional Chinese medicine acupoint selection rule, a personalized three-dimensional human body model is constructed, and acupoints are accurately calibrated; real-time registration and shielding processing of the virtual model and a real scene are achieved through mixed reality equipment; various natural interaction modes such as gestures, voices and sight lines are supported, and an integrated function of learning, training and assessment is provided; and the meridian qi-blood running state can be dynamically simulated based on the midnight-noon ebb-flow theory. The system has the advantages that the problems that traditional Chinese medicine teaching is poor in intuition and interactivity, cannot be personalized, is inconvenient to examine and the like are solved, and immersive, interactive and intelligent teaching of traditional Chinese medicine meridian point knowledge is achieved.

The invention discloses a method for obtaining a three-dimensional human body model, terminal equipment and cloud equipment. The method comprises the following steps: receiving a target network and a posture image of a to-be-reconstructed human body transmitted by the cloud equipment; the target network is a network obtained by training image acquisition data of a to-be-reconstructed human body, the target network is used for outputting target parameters, and the target parameters represent human body features of the to-be-reconstructed human body; obtaining a target parameter corresponding to the target network based on the attitude image and the target network; and obtaining a target three-dimensional human body model based on the target parameters.

The invention discloses an intelligent human body modeling method and system based on a three-dimensional dot matrix, and relates to the field of intelligent modeling, and the method comprises the steps: carrying out the structural analysis of the semantic level of an original three-dimensional point cloud, and predicting a space displacement field pointing from the surface of observed clothes to the surface of an internal real human body under the semantic guidance. By applying the displacement field, double transformation of point cloud stripping and standardization is carried out on the point cloud, so that repeated fine tuning and optimization are carried out on the body type and posture parameters of the human body model in a standardized space, and finally a high-fidelity human body three-dimensional model is generated through driving. In this way, the complex clothes interference problem is deconstructed into a staged geometric mapping and parameter optimization process, and therefore the robustness and accuracy of human body three-dimensional reconstruction under the real wearing condition are effectively improved.