378 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 provides a virtual digital population type synchronization method, system and device, a medium and a program product, and the method comprises the following steps: analyzing an input voice signal, and obtaining a voice feature; taking the voice features as input of a lip movement prediction model, and predicting corresponding lip movement parameters; driving a face model of the virtual digital human to perform real-time rendering according to the lip movement parameters, and realizing fine movement control of lips; and obtaining user feedback data, and continuously optimizing and adjusting the lip movement prediction model based on the user feedback data. Through advanced technologies such as deep learning, voice analysis and lip movement prediction, accurate synchronization of the virtual digital population type and the voice content is realized, and the expression ability of the virtual character and the user interaction experience are improved.

The invention discloses a shaping scheme generation method and system based on image recognition, and the method comprises the steps: obtaining a face image of a user, constructing a half-face model, generating a similar face set, recommending the similar face set to the user, generating a full-face comparison image set, screening the full-face comparison image set, and determining a target case. The method comprises the steps of obtaining a user face three-dimensional data model, predicting a reshaped face image, training a conversion model for converting a plane face image into a three-dimensional face model, converting the predicted reshaped face image into a reshaped three-dimensional face model, and generating a reshaping scheme for the user based on the user face three-dimensional data model and a target three-dimensional face model. Objective, personalized and high-precision generation of the plastic scheme is realized, a user can intuitively preview the plastic effect and participate in formulation of the scheme, satisfaction and credibility are improved, plastic doctors can formulate more professional and feasible operation plans based on detailed scheme and data support, operation risks are reduced, and the success rate is improved.

The invention discloses an emotion recognition analysis method for expression data based on artificial intelligence, and relates to the technical field of image recognition, the method comprises the steps: collecting the facial contour of a target person, constructing a facial model, selecting a plurality of traction points corresponding to the facial model, and carrying out the combination according to the corresponding emotions of the traction points, and obtaining a combined point cloud of a plurality of corresponding emotions; establishing a pull line relationship between the combined point clouds of the plurality of corresponding emotions and the face model; collecting face emotional expression video image data of the target person in a preset time period, and fitting the face emotional expression video image data with the face model to obtain a fitting model; and based on the face model in the fitting model, obtaining a combined point cloud of a corresponding emotion through a pull line relationship. According to the method, the emotion recognition data can be embodied and cleared, the emotion types which do not correspond to the target person can be deleted and selected in the dynamic analysis process of the face emotion expression video image data of the target person, and the accuracy of emotion type recognition can be ensured.

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 invention provides a desktop digital human interactive studio method based on JavaFX, and the method comprises the steps: scanning the face of a real person through a depth camera, obtaining the three-dimensional shape data of a facial tissue, carrying out the subdivision and marking of the data through the combination of medical anatomical knowledge, and building a digital human face model which comprises the geometric shapes and texture features of lip, tongue and tooth facial tissue structures; establishing a tooth visibility control rule according to the mapping relation between the tongue position and the phoneme in the playing process, and dynamically adjusting the tooth visibility of the digital person in the playing process according to the tooth visibility parameter of the phoneme sequence, so that the tooth visibility change is matched with the characteristics of the real person in the playing process; and tracking the shape and texture features of the lip, tongue and tooth areas of the digital person in the playing process in real time, comparing the shape and texture features with target playing lip shape, tongue position and tooth visibility parameters, dynamically adjusting the parameters of the digital person face model, and performing cooperative control of lip, tongue and teeth in the playing process.

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.

A method for controlling an artificial intelligence (AI) device can include receiving, by a processor, an input two dimensional (2D) image, encoding the 2D image to generate an image feature map, obtaining UV positional encoding information based on a three-dimensional (3D) face model where the UV positional encoding information includes a UV feature map, generating, by the processor, a correlation volume based on the image feature map and the UV feature map, generating flow map information and uncertainty information based on the correlation volume and the UV positional encoding information, and generating probabilistic 2D alignment information based on the flow map information and uncertainty information and outputting the probabilistic 2D alignment information. Also, the method can further include generating 3D reconstruction information based on the probabilistic 2D alignment information for animating a 3D face based on the input 2D image.

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 face recognition method and device based on a three-dimensional mixed deformation model, and belongs to the field of computer image processing. A hair style matching step and an image matching step. According to the invention, accurate feature migration can be carried out between face models of different styles, so that the features of a real face can be accurately reflected in a cartoon style model.

The invention discloses a three-dimensional sight line redirection method based on a neural radiation field, and the method comprises the following steps: S1, collecting and processing a data set, and carrying out the face model processing of an image in the data set; s2, carrying out feature coding and carrying out constraint learning on a multi-layer perceptron (MLP); s3, extracting a three-plane feature T by using a three-plane feature extractor EF through the image and the three-dimensional face parameters obtained in the step S1, and performing volume rendering through the three-plane feature T to generate a low-resolution feature FT; and S4, performing model overall optimization by executing a gradient descent method to update the overall weight of the model, and performing iterative training for a plurality of rounds. According to the three-dimensional sight line redirection method based on the neural radiation field, end-to-end sight line redirection model training is achieved, explicit decoupling of the head posture and the gazing direction is achieved under the condition that a single-view-angle image is used, and the effect of controlling the gazing direction and the head posture at the same time is achieved.

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 computer vision, in particular to a three-dimensional facial animation generation method and system based on 3DMM, and the method comprises the steps: constructing a three-dimensional facial model; obtaining a target audio, extracting emotional features in the target audio, and inputting the emotional features into the trained ExpNet network to obtain a predicted expression coefficient; applying the predicted expression coefficient to a three-dimensional face model to generate a facial expression of the three-dimensional face model; and inputting the target audio into a trained PoseVAE network to obtain predicted head posture information, and applying the predicted head posture information to a three-dimensional face model to obtain a head animation. In the facial animation generation process, the internal relation between the audio and the facial expression is fully considered, automatic, high-accuracy and diversified generation effects are achieved through the deep learning model, and more real and natural facial animation experience is provided for a user.

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.

The invention provides an oxygen mask optimization design method based on a finite element simulation technology. The method comprises the specific steps that S1, an oxygen mask three-dimensional model is constructed based on an obtained face model; s2, simulating the oxygen mask by using a finite element simulation technology to obtain accurate pressure distribution; and S3, optimizing the shape of the oxygen mask based on a numerical gradient method to obtain an optimal shape of the oxygen mask. The oxygen mask can be designed in a customized mode, the pressure distribution of the optimized oxygen mask is more uniform, the area of a high-pressure area is reduced, and the oxygen mask can better adapt to the face shape of a patient, so that the wearing comfort is improved, and the risk of pressure injury is reduced.

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 discloses a cattle face recognition method based on dense point cloud three-dimensional reconstruction, which comprises the following steps: S1, receiving four cattle pictures from different visual angles to capture detailed characteristics of cattle, S2, converting the cattle pictures from different visual angles into 3D dense point cloud coordinates by using a 3D reconstruction technology to ensure high efficiency and accuracy of face recognition, S3, extracting constructed 3D dense point cloud characteristics, and S4, extracting the 3D dense point cloud characteristics from the 3D dense point cloud characteristics. And performing cattle face comparison. Based on the 3D reconstruction technology, a high-quality 3D cattle face model is generated through a limited number of 2D cattle face images, further the 3D features of the cattle face are deeply mined and analyzed, accurate face information comparison is achieved, and cattle identity recognition is completed. The method improves the recognition accuracy and robustness through 3D feature comparison, can be suitable for file establishment, transaction and traceability in the cattle feeding process, is mainly used for cattle identity uniqueness recognition and authentication, is also called as living asset uniqueness authentication, and is used in multiple fields such as insurance and loan in scenes. And the traditional industry is enabled by means of tools of science and technology and general finance.

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.

Systems and methods for generating a face model for a user of a head-mounted device are disclosed. The head-mounted device can include one or more eye cameras configured to image the face of the user while the user is putting the device on or taking the device off. The images obtained by the eye cameras may be analyzed using a stereoscopic vision technique, a monocular vision technique, or a combination, to generate a face model for the user. The face model can be used to generate a virtual image of at least a portion of the user's face, for example to be presented as an avatar.