202 results about "Face model" patented technology

The invention discloses a buccal needle positioning navigation method and system based on multi-mode perception driving, and the method comprises the steps: collecting the image information and depth information of the face of a patient; generating an individualized face three-dimensional model according to the image information and the depth information; determining a standard meridian map according to a meridian database, and registering the standard meridian map to the individualized face three-dimensional model through non-rigid deformation to obtain acupuncture point positioning; according to the acupuncture point positioning and a pre-stored organization hierarchy model, needle insertion path guiding information is calculated and generated, and in an interactive interface, the acupuncture point positioning and the needle insertion path guiding information are displayed in an overlapped mode so as to execute the needle insertion guiding process; and in the needle insertion guiding process, collision detection is carried out according to the needle insertion path guiding information and the organization level model, and safety early warning is carried out when a collision risk is detected. The precision and safety of the acupuncture treatment process can be effectively improved.

The embodiment of the invention provides a face texture processing method and device, equipment, a storage medium and a product. Comprising the following steps: inputting a to-be-processed two-dimensional face image into a pre-trained detailed expression and rough shape model, and outputting skin color texture parameters and contour features; obtaining a target three-dimensional face grid from a pre-selected three-dimensional deformable model according to the contour features, and extracting a reflection attribute of each vertex in the target three-dimensional face grid; generating a reflection characteristic mapping graph according to the reflection attribute of each vertex; inputting the skin color texture parameters, the reflection characteristic mapping graph and preset noise into a pre-trained basic texture generation network to output a face basic texture structure; inputting the to-be-processed two-dimensional face image into a pre-trained high-frequency detail extraction network to output high-frequency detail features; and inputting the face basic texture structure and the high-frequency detail features into a pre-trained feature fusion network to output an optimized face texture map, thereby achieving the effect of improving the precision of the three-dimensional face model.

The embodiment of the invention discloses a cerebral apoplexy upper limb rehabilitation training system and method based on dynamic reward feedback. A data acquisition module is used for acquiring physiological signals, motion signals, emotional state data and training performance data; the motion intention decoding module is used for performing motion intention feature extraction and reliability evaluation on the physiological signals according to an improved MREE-Net + + fusion algorithm, performing weighted fusion after feature weights are adjusted according to a reliability result, obtaining a unified motion intention feature vector, performing motion intention decoding, and obtaining a motion intention intensity index and a motion intention vector; the dynamic reward decision module is used for processing the facial micro-expression data according to an improved VGG-Face model to obtain an emotion titer, and obtaining an emotion awakening degree according to the voice signal; a reward action is generated according to an improved deep reinforcement learning algorithm; and the adaptive training regulation and control module is used for generating a personalized virtual training scene according to the generative adversarial network and regulating and controlling the training intensity according to the fatigue index. The rehabilitation training effect can be improved.

The invention discloses a virtual image clothes changing method and system, and the method comprises the steps: carrying out the preprocessing of a figure image inputted by a user, and obtaining standardized image data; recognizing a face region in the standardized image data, and constructing a three-dimensional face model; body parameters corresponding to the three-dimensional face model are determined, and a three-dimensional human body model is generated based on the body parameters; based on a matching result between the measured size of each key part in the three-dimensional human body model and a design size of a corresponding part in a preset clothing template, deforming the clothing template to obtain a three-dimensional clothing model, and simulating virtual clothing changing effect data when the three-dimensional human body model wears the three-dimensional clothing model; and updating the three-dimensional human body model according to the facial features of the user and the body posture of the user, updating the virtual clothes changing effect data according to the updated three-dimensional human body model, and obtaining a virtual image preview picture corresponding to the dynamic performance behavior of the user.

The invention relates to a method for identifying a person in the vicinity of a motor vehicle using sensors arranged on the vehicle, the method comprising the following steps when the person approaches the motor vehicle: a) detecting their gait by means of the sensors, analysing at least one gait-related parameter, comparing it with previously recorded and established gait models and calculating a gait recognition score; b) detecting the face of the person by means of the sensors, analysing at least one parameter of the face, comparing it with previously recorded and established face models and calculating a face recognition score; c) normalising the scores according to reference scores; d) fusing the normalised gait recognition and face recognition scores in order to identify whether or not the person is associated with the motor vehicle.

The invention relates to the technical field of image reconstruction, in particular to a face multi-camera reconstruction system and method, and the method comprises the steps: S1, recognizing a face dynamic region in an input video stream in real time through a semantic segmentation network, generating a collection instruction, and controlling a multi-camera system to synchronously collect a multi-view image sequence; s2, performing calibration parameter optimization on the multi-camera system by using a multi-face three-dimensional deformable model as a semantic calibration target, and performing three-dimensional reconstruction on the multi-view image sequence based on the optimized parameters to generate an initial three-dimensional face point cloud; and S3, according to the semantic information of the face dynamic region, carrying out feature weighted fusion on the generated initial three-dimensional face point cloud, inhibiting the noise of the dynamic region, enhancing the details of the static region, and finally generating a high-precision three-dimensional face model. According to the method, the calculation efficiency is maintained, the reconstruction quality and the reality sense are considered, and the high-precision three-dimensional modeling capability for the complex dynamic face scene is realized.

Provided is an electronic device for 3D consistent 2D landmark generation for facial images. The electronic device acquires image data of a face of a person from an image-capture system and determines a first plurality of two-dimensional (2D) facial landmarks based on the image data. Further, the electronic device obtains a 3D face model of the face based on the acquired image data and determines a plurality of 3D facial landmarks on 3D face model. The electronic device compute 3D attribute information is computed based on statistical information associated with neighboring 3D points of 3D face model around corresponding 3D facial landmark of plurality of 3D facial landmarks. Furthermore, electronic device generate input based on application of encoding operation on computed 3D attribute information and determined plurality of 2D facial landmarks and generate second plurality of 2D facial landmarks based on application of neural network-based landmark detector on generated input.

The invention relates to a personalized virtual makeup trying system and method based on multi-modal interaction and three-dimensional reconstruction, and the method comprises the steps: collecting a face video stream through a mobile terminal by a camera capture module, and generating multi-modal sensing data through space-time alignment; the three-dimensional reconstruction module fuses the expression change and the head motion trail to construct a dynamic face model; the sensor fusion module analyzes a reference makeup image, a voice instruction and an environment illumination parameter uploaded by a user, and generates a makeup volume measuring scheme through cross-modal semantic fusion; the interface synchronization module simulates the physical interaction effect of skin and cosmetics on the dynamic face model to generate an environment-adaptive virtual makeup trying effect, so that the problems of dynamic distortion, multi-mode intention splitting, poor environment adaptability, insufficient makeup effect physical restoration and the like in the traditional makeup trying technology are solved; makeup fitting, complex intention analysis, illumination change real-time adaptation and real restoration of cosmetic materials during large-angle rotation of the head are achieved, and the device can be widely applied to the fields of beauty makeup retail, medical beauty rehabilitation and the like.

The invention discloses a face recognition method and device, computer equipment and a storage medium, traditional two-dimensional image recognition is expanded into three-dimensional face structure modeling, and high-dimensional face feature data is jointly constructed by using skeleton information of key nodes and high-discrimination characteristics of iris textures. Meanwhile, local muscle dynamic analysis is performed through a micro expression recognition model, and dynamic changes are mapped to a three-dimensional model for geometric fine adjustment, so that a final face model not only has static structure consistency, but also can reflect facial fine muscle changes. And the three-dimensional model and the standard model in the database are registered and compared in a unified space, so that the accuracy and stability of identity confirmation are remarkably improved. According to the invention, full-link optimization from image preprocessing, feature construction, three-dimensional modeling to identity comparison in a face recognition process is realized, and the face recognition reliability in a complex scene is greatly improved.

The invention relates to a method and equipment for determining the position of a blasting bead in a cigarette filter tip, and the method comprises the steps: collecting data information of a cigarette from different angles in the circumferential direction through CT equipment, and constructing a three-dimensional reconstruction model of the cigarette based on the data information; splitting a filter tip area and a blasting bead area from the three-dimensional reconstruction model according to gray difference, and respectively forming a point cloud data set of the filter tip area and a point cloud data set of the blasting bead area; circle fitting is conducted on the point cloud data of the smoking end of the cigarette in the point cloud data set of the filter tip area, an end face model is obtained, and in the vertical state of the three-dimensional reconstruction model, the smoking end is the highest position or the lowest position in the filter tip area according to the cigarette direction of the three-dimensional reconstruction model; performing sphere fitting on the point cloud data set of the bead blasting area to obtain a sphere center of a fitted sphere; and calculating the distance from the center of sphere to the end face model to obtain the distance from the blasting bead to the suction end, thereby improving the efficiency and accuracy of position determination.

Embodiments of the present disclosure disclose a three-dimensional face model reconstruction method. A specific implementation of the method includes: generating each initial face key point, each initial face segmentation region, and an initial face normal; obtaining a rough fitting three-dimensional face model and shape parameters, pose parameters, expression parameters, and a model vertex coordinate matrix; obtaining depth feature information and pose feature information; obtaining fused feature information; generating a vertex offset matrix; obtaining a reconstructed three-dimensional face model, and generating each face key point, each face segmentation region, a face normal, and face depth data; generating a face model loss value; determining the reconstructed three-dimensional face model as a three-dimensional face model; and using the reconstructed three-dimensional face model as an initial three-dimensional face model. The implementation can perform nonlinear deformation on the three-dimensional model, thereby obtaining a three-dimensional face model that is more similar to the original picture.

The invention discloses a structured light and texture feature fused three-dimensional face model registration method and system, and relates to the field of face model registration, and the method comprises the steps: firstly carrying out the point-by-point analysis of a source model, and calculating the significance degree of local geometric features and the richness degree of texture information of the source model; an exclusive confidence score is generated for each point on the model. Then, respective geometric and luminosity weights are dynamically generated for each point based on the confidence score. A higher geometric weight is automatically given to an area with a prominent geometric structure and a single texture; however, the luminosity weight can be improved in a region which is geometrically flat but rich in texture. Finally, the group of adaptive weights capable of accurately reflecting the reliability of local information are integrated into a fusion cost function, and a registration optimization process is guided to intelligently focus on a more reliable information source, so that interference caused by feature imbalance is effectively overcome, and three-dimensional face model registration with higher precision and robustness is realized.

The invention provides a rapid three-dimensional face reconstruction method and system based on left and right face depth cameras, and the method comprises the steps: collecting gray-scale maps, RGB images and three-dimensional point clouds of two sides of a face through synchronously triggering the left and right depth cameras; detecting a human face ROI based on the feature points and segmenting the preprocessed point clouds to obtain left and right face point clouds; carrying out coarse and fine registration on the left and right face point clouds through a registration algorithm, and carrying out Poisson reconstruction after fusion to generate a three-dimensional face grid model; positioning a texture area in the RGB image according to the human face ROI, and processing and splicing RGB textures to generate a complete texture map; and dividing vertexes of the three-dimensional face mesh model according to the middle axial surface of the nose tip, eliminating texture splicing gaps, calculating texture coordinates, and then outputting a three-dimensional face model with textures. According to the method, a double-camera system is adopted, rapid, high-integrity, high-precision and texture-seamless three-dimensional face reconstruction is realized, and the hardware cost and the calculation complexity are effectively reduced.

The invention provides a virtual character face editing method and device, electronic equipment and a storage medium. A plurality of editing control points are displayed on a face model; in response to a selection operation for the target editing control point, displaying a grid prompt of a target facial feature region corresponding to the target editing control point; and in response to a dragging operation on the target editing control point, adjusting the target facial feature region according to the direction and / or distance of the dragging operation, and updating the deformation and / or color prompted by the grid in real time to reflect the adjustment. By means of the method, a player can directly drag the corresponding control points to adjust the facial features, compared with a traditional slider operation mode, the direct dragging interaction mode better conforms to operation habits of a user, operation steps and learning cost are reduced, and interaction experience is remarkably improved; and meanwhile, a visual feedback mechanism of grid prompt is combined, so that the player can see the change of the adjustment area in real time, and the user experience is improved.

The invention relates to the technical field of computer vision and computer graphics, in particular to a monocular image-driven efficient three-dimensional face modeling and rendering method, which comprises the following steps of: acquiring a plurality of monocular portrait images and annotation information acquired by a camera, and establishing a portrait data set; establishing a generative model, wherein the generative model generates a portrait image from the Gaussian noise; pre-training the generated model to obtain a pre-trained 3D decoder; establishing a three-dimensional Gaussian reconstruction model, wherein the three-dimensional Gaussian reconstruction model generates a portrait image from the input image; performing joint fine tuning on the three-dimensional Gaussian reconstruction model to obtain a trained three-dimensional Gaussian reconstruction model; inputting a to-be-reconstructed monocular image into the trained three-dimensional Gaussian reconstruction model, and performing reasoning and rendering to obtain a reconstructed portrait image; according to the method, the training data collection difficulty can be reduced, and the three-dimensional face reconstruction generalization is improved.

The application provides a kind of expression action control method and device for three-dimensional cartoon production, comprising: step S1, obtaining the description vocabulary of the current to-be-produced character of the to-be-produced three-dimensional cartoon;Step S2, obtain the historical wind evaluation related to the description vocabulary consistent with the cartoon character and the facial expression of the cartoon character, filter out the top N cartoon characters of historical wind evaluation as positive reference characters, N is a positive integer;Step S3, obtain the basic face model of the current to-be-produced character, respectively migrate the facial expression action of all positive reference characters to the basic face model of the current to-be-produced character, obtain and control the facial expression action of the current to-be-produced character is generated.The facial expression action of the application is good by migrating the facial expression action of the historical wind evaluation of the cartoon character to the basic face model of the current to-be-produced character, thereby improving the facial expression action of the three-dimensional cartoon production.

The application provides a three-dimensional face animation generation method and system based on physiological prior art, and belongs to the fields of computer vision and computer simulation, and comprises the following steps: S1: obtaining an initial three-dimensional face model based on CT and MRI scanning section images; correcting, mesh processing and filling the initial three-dimensional face model to obtain a three-dimensional face physiological model; S2: using an unstructured Delaunay algorithm to realize unstructured tetrahedral partitioning on the three-dimensional face physiological model to obtain a three-dimensional face physiological model mesh partitioning result; S3: performing biomechanical modeling on the three-dimensional face physiological model mesh partitioning result; S4: adding contact constraints and boundary conditions between different tissues of the three-dimensional face physiological model to construct a finite element analysis model to simulate facial deformation; and S5: solving and analyzing the finite element model to obtain a three-dimensional face animation. The method can generate high-quality three-dimensional face animations based on a small amount of samples.

The invention provides a face key point recognition method and device, equipment and a storage medium, and the method comprises the steps: collecting a to-be-recognized face image, and extracting a first image feature map of the to-be-recognized face image; target camera parameters corresponding to the face image to be recognized are recognized through the first image feature map; mapping the face key points of the preset 3D universal face model to a 2D plane through the target camera parameters to obtain coarse positioning face key point coordinates of the to-be-recognized face image; generating a second image feature map through coarse positioning of the face key point coordinates; performing feature fusion on the first image feature map and the second image feature map to obtain a fused image feature map; by fusing the image feature map, the fine positioning face key point coordinates of the face image to be recognized are recognized, so that the recognition precision of the face key point coordinates is improved.

The invention provides an object rendering method and device, electronic equipment, a computer readable storage medium and a computer program product. Application to the technical field of computer graphics. The method comprises the following steps: acquiring a three-dimensional model of a virtual object to be rendered; performing surface reduction processing on the three-dimensional model to obtain a surface reduction model; determining vertex data of the virtual object to be rendered based on the subtraction model, the vertex data including vertex positions and skin weights of a plurality of vertexes; differential fitting processing is carried out on the vertex position and the skin parameters to obtain vertex data after fitting; and based on the fitted vertex data, performing rendering processing on the to-be-rendered virtual object to obtain a rendered virtual object. According to the method and the device, the rendered virtual object obtained after the surface reduction processing can still achieve the original rendering effect before the surface reduction processing in the visual effect.

The application discloses a high-fidelity three-dimensional face reconstruction and generation method based on an implicit neural function, and proposes a high-fidelity parameterized face model which can be semantically decoupled and edited, and simultaneously introduces new identity, expression representation and semantic segmentation networks. With the aid of the implicit neural representation, the method can perform high-fidelity reconstruction and synthesis on face details, difficult expressions and large-angle camera poses, so that the generated face pictures have good reality and clarity.

The present application belongs to the technical field of image processing, and specifically relates to a three-dimensional face model automatic generation method, which is characterized by the following specific steps: data loading processing, user model generation based on computer vision, user model grid re-topology, model grid local shape optimization, model grid fusion splicing, and output result model. The grid generation method ensures 2D mapping accuracy according to face key points; the grid deformation and smoothing method makes the user model and the standard face model closer in 3D space under the premise of reasonable deformation, so that the mapping is more accurate; the re-topology model grid local shape optimization utilizes original grid information to correct the grid deformity and improve the quality of the model. The model grid fusion splicing adopts the grid deformation and grid smoothing method, so that the output result meets the design requirements, guarantees the integrity and high quality of the model, and reduces the post-processing work.

Embodiments of the present application provide a face image generation method, an electronic device and a computer readable storage medium, and relate to the technical field of artificial intelligence. The method comprises: acquiring a source face image of a preset object; inputting the source face image into a preset three-dimensional face model for face modeling processing to obtain a three-dimensional face prior feature; performing feature extraction on the source face image to obtain a source face image feature; inputting the three-dimensional face prior feature and the source face image feature into a preset heterogeneous convolution model for face local feature refinement processing to obtain a target face fusion feature vector; inputting the target face fusion feature vector into a preset generator for face reconstruction to obtain a candidate face image; and adjusting the generator through a perceptual loss between the source face image and the candidate face image to obtain a target face image. In the embodiments, the local detail quality of the face image can be improved while ensuring the identity consistency of the face image.

The invention provides a face capacity grid editing method of a digital human face model, a digital human face model determination method, a digital human model determination method and a digital human video generation method, and relates to the technical field of digital humans. The operation complexity of a user can be reduced, the user does not need professional art knowledge, the use threshold is low, and the editing freedom degree is large. After the target dominant parameter of the user is received, the target dominant parameter is combined with the initial implicit parameter of the initial face grid to generate the target face grid, so that the editing requirement of the user can be met, the diversified target face grid meeting the user requirement can be generated, the overall coordination of the target face grid can be ensured, and the user experience is improved. The situation that the face grid is not coordinated due to the fact that the user edits a large number of face parameters is avoided, the target face grid conforms to the aesthetic appreciation of the user, and the satisfaction degree and the use experience of the user are improved.

The embodiment of the application provides a virtual object face changing method and device, computer equipment and a storage medium, which can quickly determine the target face shape of the face image based on the face shape mapping relationship set of the virtual object to be changed and the target three-dimensional dynamic face model corresponding to the face shape feature information of the face image, so that the face changing of the virtual object to be changed can be performed based on the target face shape and the target texture, and the face changing efficiency of the three-dimensional virtual object is improved.

The invention discloses a face detection method based on a Mamb-YOLO-Face model, deep semantic features are efficiently extracted through a BidGSUSSM backbone network, key information such as textures and edges of a face is captured, background noise interference is effectively reduced, the detection precision is improved, and meanwhile, the network reduces redundant calculation and optimizes the structure, so that the detection accuracy is improved. Compared with the prior art, the C2f-BidGSUSSM neck network has the advantages that model parameters and calculation complexity are reduced, balance of precision and efficiency is realized, in addition, the BidGSUSSM adaptively adjusts sensitivity to illumination and postures by using multi-scale feature extraction and an attention mechanism, false detection / missing detection caused by illumination changes or side faces is reduced, and the C2f-BidGSUSSM neck network has the advantages that the C2f-BidGSUSSM neck network is optimized through cross-scale feature fusion, so that the accuracy and accuracy of the C2f-BidGSUSSM neck network is improved. Compared with the prior art, the 5Point Landmark Regression Head network has the advantages that the detection capability on small targets and shielded faces is enhanced, the omission ratio is remarkably reduced, the 5Point Landmark Regression Head network optimizes the detection frame positioning precision by regressing face key points, and the robustness on posture change and shielded faces is improved by using key point constraints.

Embodiments of the present application provide a face customization method and device, electronic equipment and readable medium, the method comprises: obtaining a face contour model and at least one facial feature model; merging contour vertex information of the face contour model with facial feature vertex information of the facial feature model to obtain face vertex information of a face model; merging contour texture maps corresponding to the face contour model with facial feature texture maps corresponding to the facial feature model to obtain a face texture map corresponding to the face model; and rendering the face model using the face vertex information of the face model and the corresponding face texture map. The face customization function with greater customization range and higher degree of freedom can be provided for the user, and the face customization effect can be better.

The application relates to the technical field of CAE preprocessing and cloud cooperation, and discloses a lightweight CAE geometry engine and a cloud real-time cooperation method and system. A cloud server acquires an original CAE geometry model, performs topological simplification and data compression on the original CAE geometry model, and generates a lightweight geometry data file containing multiple resolution levels; the cloud server pushes the received difference data packet to other online cooperation terminals; the other cooperation terminals parse the unique version number carried by the difference data packet through a locally loaded WebAssembly geometry engine module, and perform operation replay updating on the local geometry model according to the differential data contained in the difference data packet. According to the scheme, the original CAE model data volume is reduced by 90%-95%, the WebAssembly engine realizes a 100,000-face model rendering frame rate of 30-60 fps, and the operation response delay is less than or equal to 100 ms, thereby breaking through the performance bottleneck of browser-side geometry processing.

Provided is an electronic device for 3D consistent 2D landmark generation for facial images. The electronic device acquires image data of a face of a person from an image- capture system and determines a first plurality of two-dimensional (2D) facial landmarks based on the image data. Further, the electronic device obtains a 3D face model of the face based on the acquired image data and determines a plurality of 3D facial landmarks on 3D face model. The electronic device compute 3D attribute information is computed based on statistical information associated with neighboring 3D points of 3D face model around corresponding 3D facial landmark of plurality of 3D facial landmarks. Furthermore, electronic device generate input based on application of encoding operation on computed 3D attribute information and determined plurality of 2D facial landmarks and generate second plurality of 2D facial landmarks based on application of neural network-based landmark detector on generated input.

The application provides a pet face and body modeling identity card generation method based on an AI knowledge graph, and belongs to the technical field of AI modeling and identity generation. The method comprises the following steps: constructing a pet knowledge graph database containing morphological correlation parameters of different breeds and different growth stages; on the basis of collecting pet face and body images, combining attribute information such as breed, gender and birth date, generating a graph embedding vector that fuses visual features and dynamic growth information, and performing time sequence correction processing on the embedding vector according to the current growth stage of the pet, finally generating a baseline identity card code and a stage identity card code that can be updated with the growth stage, and effectively solving the contradiction between the static pet identity card and the dynamic pet appearance in the prior art through a double-layer coding mechanism.

The invention relates to a face modeling method and device and a storage medium. The method comprises the following steps: determining a first human face three-dimensional model, and obtaining a human face image uploaded by a user; generating a target face three-dimensional grid model based on the first face three-dimensional model and the face image; and determining texture parameters and material parameters of the target face three-dimensional grid model based on the face image, and generating a second face three-dimensional model. According to the embodiment of the invention, the stylized three-dimensional face model can be automatically and efficiently generated based on the two-dimensional face image provided by the user, the user can efficiently and accurately obtain the expected three-dimensional face appearance without professional modeling experience or manual complex parameter adjustment in the process, and the user experience is improved. And the creation threshold of the high-quality three-dimensional face content is obviously reduced.