19 results about "Facial reconstruction" patented technology

The invention provides a medical beauty-oriented AI face diagnosis feature and aesthetics analysis method and system, and relates to the technical field of intelligent medical beauty, and the method comprises the steps: collecting and preprocessing user face image data to obtain standardized face data; constructing a time sequence feature extraction model based on a deep convolutional neural network, and extracting facial dynamic features in combination with an optical flow estimation algorithm; and constructing a face reconstruction model based on the depth estimation network, and reconstructing a face three-dimensional structure by using a back projection mechanism and a geodesic distance partitioning strategy. According to the invention, accurate analysis of facial features can be realized, and the accuracy and individuation level of medical beauty diagnosis are improved.

This application discloses a method and apparatus for training a facial modeling model, a modeling method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product, which relate to the field of artificial intelligence. The method includes the steps of: acquiring sample facial images, where the sample facial images include facial images of the same object at the same time and from different viewpoints; performing feature encoding on the sample facial images by an encoder of the facial modeling model to obtain facial region latent codes corresponding to different facial regions and facial action latent codes corresponding to facial actions; performing 3D facial reconstruction on the object based on the facial region latent codes and the facial action latent codes via a neural radiation field of the facial modeling model to obtain a 3D facial image of the object; obtaining a 3D reconstruction loss between the 3D facial image and the sample facial image; and training the facial modeling model based on the 3D reconstruction loss.

This paper proposes a method for reconstructing the faces of curling athletes based on broadcast videos. This method optimizes the facial features of curling athletes in broadcast videos and utilizes deep learning techniques combined with a mask network strategy to overcome the limitations of traditional methods in processing reflective and weakly textured areas. This method provides more realistic and vivid digital human images for applications such as the metaverse, digital humans, and sports broadcasts.

The present disclosure provides a facial alignment tool to project a laser line or multiple lines onto a patient's face to assess symmetry and / or alignment during facial and / or nasal plastic surgery procedures such as rhinoplasty, nasal reconstruction, facial reconstruction, among other cosmetic or functional facial plastic surgery procedures. The facial alignment tool includes a moldable mouth guard specifically fabricated to be received in a patient's mouth, a support plate configured to receive the moldable mouth guard, a flange rigidly attached at its proximal edge to a front of the support plate, a positioning component attached to the distal edge of the flange, a rigid beam rigidly attached at its proximal end to the positioning component such that actuating the positioning component laterally translates the rigid beam, and a light source mounted to a distal end to the rigid beam by an adjustable laser mount configured to allow translation and rotation of the laser light source in the adjustable laser mount. The laser source is mounted such that when a patient has the support plate with the moldable mouth guard in their mouth the light source emits visible light projected onto the patient's face, and adjusting the positioning components moves the rigid beam and the light source attached thereto with respect to the patient's face.

A fatigue-state detection method. The method comprises: acquiring facial key points of a user to be subjected to detection; on the basis of the facial key points and preset facial parameters, performing three-dimensional facial reconstruction on said user, so as to determine facial expression coefficients, wherein the preset facial parameters are facial feature information obtained after three-dimensional modeling is performed on a facial image; performing serialization processing on the facial expression coefficients within a set time, so as to obtain a timing sequence of the facial expression coefficients; and on the basis of the timing sequence of the facial expression coefficients, using a pre-trained detection model to detect a fatigue state of said user within the set time, so as to obtain a fatigue detection result.

The present invention relates to the field of artificial intelligence technology, and specifically provides a method, device, electronic device and medium for three-dimensional face reconstruction, aiming to solve the technical problem of low accuracy of face reconstruction corresponding to the existing face reconstruction methods. For this purpose, the three-dimensional face reconstruction method of the present invention includes the following steps: obtaining an original speckle infrared image containing a human face; performing face detection on the original speckle infrared image to obtain a face region; determining the matching degree between each pixel in the face region and a reference image, and obtaining a depth map from the face region by using the matching degree; obtaining a high-resolution three-dimensional face image based on the depth map. In this way, the accuracy of three-dimensional face reconstruction is improved.

A three-dimensional head model generation method based on 2D facial expression analysis includes four steps of expression analysis, occlusion processing, multi-angle image generation and 3D model optimization. First, the expression parameters are extracted by the expression recognition model, and the expression is neutralized to generate expressionless images. Then, the image segmentation and deep learning inpainting technology are used to automatically identify and remove the occlusion, and the facial texture continuity is maintained. Then, multi-angle views are generated from the unoccluded neutral expression images to ensure the consistency of facial geometry and texture. Finally, the differentiable rendering technology is used to optimize the shape and map of the 3D model, realizing high consistency with the 2D image, so as to obtain high-precision and high-fidelity 3D head model. The multi-angle optimization method combines differentiable rendering with deep learning to improve the accuracy and consistency of 3D model generation. The neutral expression generation reduces geometric distortion, and the automatic removal of occlusion effectively improves the accuracy of facial reconstruction. It has strong practical value.

This invention discloses a method and apparatus for facial motion and expression transfer with detail optimization and compensation. The invention uses a 3D facial reconstruction model to model and encode facial motion and expression, achieving facial information acquisition and compression. A sliding window is used to ensure the consistency of the characters in the generated video. Multiple deep information fusion techniques are used to obtain the facial pixel flow field, initially distorting the original image to address the problems of poor generalization ability and missing motion and expression in keypoint-based expression transfer models for faces with significant differences. Multi-level facial detail encoding and optimization compensation models are used to specifically repair missing parts of the image, solving problems such as detail loss and edge distortion caused by information deficiency.

This application discloses an image generation method and related equipment. The relevant embodiments can be applied to various scenarios such as cloud technology, artificial intelligence, smart transportation, and assisted driving. The method can obtain the original facial image frame of the target object and the audio driving information of the target facial image frame to be generated; perform spatial feature extraction on the original facial image frame to obtain the original facial spatial features; perform temporal feature extraction on the audio driving information to obtain the facial local posture features; and perform facial reconstruction processing on the target object based on the original facial spatial features and the facial local posture features to generate the target facial image frame. This application can capture the facial posture details of the target object by extracting features from the audio driving information, and then perform facial adjustments on the original facial image frame based on the captured information, thereby obtaining the target facial image frame corresponding to the audio driving information. This is conducive to improving the generation efficiency and accuracy of the target facial image frame.

The application provides a medical and beauty-oriented AI face diagnosis feature and aesthetic analysis method and system, relates to the technical field of intelligent medical and beauty, and comprises the following steps: acquiring and preprocessing user face image data to obtain standardized face data; constructing a time sequence feature extraction model based on a deep convolutional neural network, and extracting face dynamic features in combination with an optical flow estimation algorithm; constructing a face reconstruction model based on a depth estimation network, and reconstructing a three-dimensional structure of the face by using a back projection mechanism and a geodesic distance partition strategy.The application can realize accurate analysis of face features and improve the accuracy and individualization level of medical and beauty diagnosis.

The invention provides a high-simulation virtual human generation method and system, and aims to gradually take a low-order virtual human as a generation source of a higher-order virtual human and carry out iterative approximation from basic image data. The technology comprises the following steps: firstly, obtaining a 2D image: step A, generating a virtual human head skeleton model file for preliminary three-dimensional face reconstruction, and then connecting the virtual human head skeleton model file to a body model bound with skeletons; b, training a neural network model for replacing the face in real time; in the step C, on one hand, the preliminary model generated in the step A is controlled through a program to realize changing actions, mouth shapes and expressions, and on the other hand, a low-order virtual human operation window is converted into virtual camera input to serve as video source input; and then performing real-time replacement with a higher simulation degree on the face by using the real-time replacement neural network model trained in the step B for the face so as to drive and form a virtual human with a higher simulation degree.

The invention discloses a restoration and 3D reconstruction method and system based on a polluted face picture, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining and preprocessing a polluted image containing a real prototype face image; processing the preprocessed face image through a preset generator-discriminator network; combining the initially generated prototype face image and the preprocessed public face data set as an input image, and processing the input image according to a parallel encoder network to obtain an initial 3D face model, a depth map and a reflectivity map; establishing a depth reflection loss function based on the depth map and the reflectivity map; and training the generator and the discriminator based on the updated generator loss function and a preset discriminator loss function to output a target 3D face reconstruction model. According to the method, the problem that the detail fidelity and the sense of reality of the 3D reconstruction result of a single polluted picture are poor is solved.

The invention discloses an image generation method and device, equipment, a medium and a program product, and relates to the field of artificial intelligence. The method comprises the following steps: encoding voice features of voice data; first face reconstruction is executed based on the voice features to obtain first face features, and the first face features are used for indicating a first face part related to the voice data; second face reconstruction is executed based on the voice feature and the first face feature to obtain a second face feature, the second face feature is used for indicating the first face part and a second face part irrelevant to the voice data, and the second face part is determined based on the anatomical relation with the first face part; and decoding the second facial feature to obtain a facial image corresponding to the voice data. According to the method, the face image can be generated based on the voice data, and the accuracy of the generated face image is improved.

Exemplary embodiments include a computer-implemented method of training a neural network for facial reconstruction including collecting a set of 3D head scans, combining each feature of each 3D head scan with a weight to create a modified set of 3D head scans, training the neural network using the modified set of head scans, and inputting a real digital facial image into the neural network for facial reconstruction. Further exemplary embodiments include the set of 3D head scans comprising approximately a tenth or less in quantity in comparison to a quantity of the modified set of 3D head scans. The modified set of 3D head scans may comprise features found in the set of 3D head scans or the modified set of 3D head scans may consist of features found in the set of 3D head scans.

The invention provides an image processing method and a related device. The method comprises the following steps: performing three-dimensional face reconstruction based on a first real image to obtain a three-dimensional face model of the first real image; projecting the three-dimensional face model to an imaging plane to obtain a first face image; performing disturbance processing on the first face image to obtain a second face image; mapping the second face image to the three-dimensional face model to obtain a three-dimensional fusion image; and determining a false face image based on the three-dimensional fused image. According to the method and the device, the relatively accurate virtual face image can be generated, so that the negative sample quality for training the face recognition model is improved, and the accuracy of model recognition can be improved.

The present application relates to an image processing method, apparatus, computer device, and storage medium. The method includes: obtaining an initial template image and an initial input image containing a facial region, obtaining facial state features of the initial template image, and obtaining initial facial shape features of the initial input image; performing three-dimensional facial reconstruction on the initial template image and the initial input image based on the facial state features and the initial facial shape features to obtain a three-dimensional reconstructed facial image; performing two-dimensional projection on the three-dimensional reconstructed facial image to obtain reconstructed facial shape features corresponding to the obtained two-dimensional reconstructed facial image; adjusting the facial region of the initial template image according to the reconstructed facial shape features to obtain a target template image; obtaining facial identity features of the initial input image, and performing fusion processing based on the facial identity features and the target template image to obtain a target image. This method can improve image processing efficiency.