305 results about "Face model" patented technology

The invention discloses an artificial intelligence assisted makeup and makeup robot collaborative makeup method, and belongs to the technical field of artificial intelligence and robots. The method comprises the following steps: collecting user face data through a multi-modal sensor, extracting multi-scale features based on improved DenseNet and HRNet networks, and generating a three-dimensional face model; a high-fidelity virtual makeup effect is generated by using a conditional generative adversarial network in combination with user requirements, and real-time comparison and display are performed through an augmented reality interface; the makeup difference is quantified through multi-dimensional feature point mapping, multi-color space analysis and texture comparison, and correction suggestions are dynamically generated; operation is executed through the seven-degree-of-freedom redundant mechanical arm, a hybrid force control strategy and a visual monitoring system are integrated, and safe and accurate execution is ensured. The method solves the problems of uncontrollable traditional makeup effect, low execution precision and lack of dynamic feedback, has the characteristics of high adaptability, real-time response and zero-damage safety, and can be widely applied to the fields of family beauty, film and television makeup and special crowd nursing.

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.

A method, system and device for generating a speckle face recognition model by using an infrared face recognition model, and a storage medium, the method comprising: step S1, obtaining an infrared face recognition model, and performing fine tuning on the infrared face recognition model through a training set to obtain a teacher face model; s2, establishing an initial student model, and aligning the output of the initial student model with the speckle feature vector; and S3, distilling an output result of the initial student model by using the speckle feature vector to obtain a final student model. According to the method, the high-quality speckle face recognition model is quickly obtained by using the high-stability infrared face recognition model, and the method has important practical significance and application value.

The invention discloses a reconstruction method and device of a three-dimensional face model and electronic equipment. The method comprises the steps that an initial parameter set used for reconstructing a three-dimensional face model is determined according to a target data set, an initial three-dimensional face model is constructed according to the initial parameter set, and the three-dimensional face model is geometric representation of a three-dimensional face corresponding to a target object; determining a projection relationship between the initial three-dimensional face model and the face images at a plurality of preset angles; adjusting the initial parameter set according to the projection relation to obtain a target parameter set, and updating the initial three-dimensional face model according to the target parameter set to obtain a target three-dimensional face model; and optimizing the target three-dimensional face model according to a preset constraint condition to obtain a three-dimensional face model. According to the method and the device, the technical problems of low reconstruction efficiency and poor precision of the three-dimensional face model due to the fact that a three-dimensional face model reconstruction method in related technologies needs to depend on complex equipment and needs a large amount of face image data are solved.

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 face three-dimensional modeling method based on an RGBD image. The face three-dimensional modeling method comprises the following steps: S100, acquiring an RGB image and depth video data by using an RGBD camera; s200, performing face detection on the RGB image, and identifying and positioning a face area; s300, face key point detection is carried out in the face area, and face key point coordinate information is extracted; s400, inputting a current depth frame in the depth video data into a face depth completion network to carry out face depth completion, inputting a completed depth image into a face depth refining network, and refining and enhancing by combining the extracted face key point coordinate information to obtain an optimized face depth image; and S500, performing multi-frame point cloud registration on the optimized face depth image in combination with the face key point coordinate information to generate a complete three-dimensional face model. According to the method, the precision and robustness of face three-dimensional modeling are further 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 discloses a moving face model modeling method and device and computer equipment. The method comprises the steps that an original video of a target object is collected, the original video is split into an image frame sequence, and the original video is a single-view-angle video; acquiring a face parameter of each image frame in the image frame sequence, and determining a camera parameter based on each image frame; applying the human face parameters to the camera parameters to obtain converted parameters, and performing three-dimensional reconstruction on the human face in the image frame based on the converted parameters and Gaussian points to obtain a human face model; and driving the face model to generate a driven face video frame. The method provided by the invention at least solves the technical problem that the construction process of the moving face model is relatively complex in the related art.

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 belongs to the field of computer vision processing, and provides a three-dimensional face generation and expression editing method and system based on text and picture driving, which comprises the following steps: extracting key description information from source face text description, generating a control vector based on common mapping of the key description information and a source face picture; enhancing the source face picture by using the control vector to obtain a source face picture after text enhancement; face model parameters are extracted based on the source face picture after text enhancement, a rough shape is generated with the face model parameters as guidance, and detail enhancement and mapping rendering are carried out on the rough shape to generate a final three-dimensional face model; generating a source face picture after expression migration according to the target face picture and the source face picture, enhancing the source face picture after expression migration by using the target face text description to obtain an expression source face picture after text enhancement, and performing three-dimensional reconstruction on the expression source face picture after text enhancement to obtain an expression source face picture after three-dimensional reconstruction. And obtaining a source three-dimensional face model after expression editing.

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.

A device may receive driver facing video data associated with a driver of a vehicle and forward facing video data associated with the vehicle, and may process the driver facing video data, with a face model, to identify driver head orientation and driver gaze. The device may generate a first transformation matrix mapping the driver facing video data, the driver head orientation, and the driver gaze, and may generate a second transformation matrix mapping the driver facing video data and the forward facing video data. The device may utilize the first transformation matrix and the second transformation matrix to estimate image coordinates, and may aggregate the image coordinates to generate aggregated coordinates. The device may generate heat maps based on the aggregated coordinates, may train machine learning model, with the heat maps, to generate a trained machine learning model, and may perform actions based on the trained machine learning model.

The invention relates to the technical field of computer vision, in particular to a facial nerve paralysis detection method and device based on a dynamic structured light technology, and the method comprises the following steps: carrying out the real-time data collection of the face of a patient, and obtaining a high-precision and complete face point cloud; obtaining a grid model based on the face point cloud; non-rigid registration is conducted on the grid model, a patient face 3D model with the same topological structure as the standard face is generated, and the patient face 3D model is a topological symmetry model; a motion vector for each point in the 3D model of the patient's face is calculated and face symmetry is quantified based on a symmetry operator. According to the method, a non-rigid registration method is used for completing transformation from a standard face model to an actual face model; and carrying out statistics on the motion vector of each point in the transformed topological symmetry model, and quantifying the facial symmetry through the information. According to the method, an objective visual tool is provided for a doctor to diagnose facial paralysis, and the doctor seeing and revising efficiency and accuracy of a patient are improved.

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.

Disclosed herein is a computing system and method configured for generating one or more personalized digital visual representations. An example computing system may be configured to receive, from a computing device, one or more photos or videos of the user, process the one or more photos or videos to determine at least facial geometry characteristics of a user face model, obtain at least facial geometry characteristics of a plurality of canon characters from a character face model, generate a fused face model to retain the facial geometry characteristics of the user face model and the character face model, generate one or more personalized digital visual representations based at least upon the fused face model, and transmit, to the computing device, the one or more personalized digital visual representations.

Disclosed herein is a computing system and method configured for generating one or more personalized digital visual representations. An example computing system may be configured to receive, from a computing device, one or more photos or videos of the user, process the one or more photos or videos to determine at least facial geometry characteristics of a user face model, obtain at least facial geometry characteristics of a plurality of canon characters from a character face model, generate a fused face model to retain the facial geometry characteristics of the user face model and the character face model, generate one or more personalized digital visual representations based at least upon the fused face model, and transmit, to the computing device, the one or more personalized digital visual representations.

The invention belongs to the technical field of artificial intelligence, and discloses a three-dimensional face reconstruction method, electronic equipment and a readable storage medium, and the method comprises the steps: determining a single two-dimensional face image; based on the two-dimensional face image, calling a three-dimensional face reconstruction model to generate a three-dimensional face model; wherein the three-dimensional face reconstruction model comprises a multi-attribute collaborative learning module, and the multi-attribute collaborative learning module is used for optimizing the initial geometric parameters to obtain target geometric parameters; during optimization, the coordinates of the three-dimensional key points are used as geometric priori to be mapped to a geometric parameter space through a multi-layer perceptron projection layer, and the correction amount of the initial geometric parameters is generated; the target geometric parameter is the superposition of the initial geometric parameter and the correction amount, and the initial geometric parameter and the coordinates of the three-dimensional key point are determined through the two-dimensional face image. According to the method, the precision of the reconstructed three-dimensional face model is improved.

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 provides a driving method of a three-dimensional face model, a training method and device of an expression simulation model, electronic equipment, a computer readable storage medium and a computer program product, and the method comprises the steps: carrying out the audio feature extraction and image feature extraction of a video, and obtaining the audio feature and image feature of a target object; predicting expression parameters of the target object and adjustment coefficients of the expression parameters based on the audio features and the image features; and driving the three-dimensional face model of the target object based on the expression parameter of the target object and the adjustment coefficient of the expression parameter. Therefore, the driving effect of the three-dimensional face model can be improved.

The utility model discloses acquisition equipment for face recognition modeling, and belongs to the technical field of face recognition acquisition. Acquisition equipment for face recognition modeling comprises a device body, a mounting cavity is formed in the device body, a through opening penetrating through the mounting cavity is formed in the device body, an arc-shaped sliding rail is formed in the bottom wall of the mounting cavity, and a driving assembly is slidably arranged at the sliding rail. The driving assembly is provided with a camera matched with the through opening and used for collecting face images, and the driving assembly is used for driving the camera to slide along the sliding rail. Through the arc-shaped slide rail and the driving assembly slidably arranged at the slide rail, the camera can move along the arc-shaped slide rail, so that multi-angle acquisition of a human face is realized, compared with fixed-angle acquisition, the multi-angle acquisition can more comprehensively capture three-dimensional features of the human face, the accuracy of three-dimensional modeling of the human face is improved, and the real-time performance of the human face is improved. And the face recognition precision is 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.

Systems and methods for generating virtual characters for video conferencing are provided. In an example, a computing device joins a video conference and determines an expression parameter vector from a video of a participant during the video conference. The device further generates a virtual character customized for the participant from a virtual character face model. The virtual character is generated by at least applying the expression parameter vector to a set of virtual character expressions customized for the participant and by incorporating a virtual character neutral face model customized for the participant. Each of the virtual character expressions describes a facial expression of the virtual character customized for the participant and the virtual character neutral face model describes a neutral face of the virtual character customized for the participant. The computing device renders the virtual character in a video stream of the participant.

A method, system and device for generating a speckle face recognition model by using an infrared face recognition model, and a storage medium, the method comprising: step S1, obtaining an infrared face recognition model, and performing fine tuning on the infrared face recognition model through a training set to obtain a teacher face model; wherein the training set comprises an infrared image and a speckle image which are aligned; the output of the teacher face model comprises an infrared feature vector and a speckle feature vector; s2, establishing an initial student model, and aligning the output of the initial student model with the speckle feature vector; and S3, distilling an output result of the initial student model by using the speckle feature vector to obtain a final student model. According to the method, the high-quality speckle face recognition model is quickly obtained by using the high-stability infrared face recognition model, and the method has important practical significance and application value.

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 present invention relates to the technical field of health detection, and is a detection model construction, detection method and related device for physical and mental state detection. The method obtains a training set and a test set, wherein each sample in the training set and the test set includes a face model feature and a true detection index; uses the training set to train a preset detection model, and outputs the trained detection model. The detection model includes an XYZ deep learning model for physiological index detection and an XYZ+RGB deep learning model for physiological and psychological index detection; uses the test set to evaluate the detection model, and reversely adjusts the detection model according to the evaluation result to output the final detection model. The present invention uses three-dimensional and RGB cameras to sample the user's face, and then combines machine learning and deep learning to detect the user's health index and psychological index, simplifies the basic physical and mental index detection process, and provides convenience for expanding the regular detection of basic physical and mental indexes.

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.