521 results about "Face model" patented technology

The present application relates to the technical field of image processing. Disclosed are a texture mapping method and apparatus based on three-dimensional (3D) face reconstruction, a device and a storage medium. The method comprises: registering a standard 3DMM model with a 3D grid model, and performing region division on the 3D grid model; obtaining a texture image of a target object, and matching 2D face key points of the texture image with 3D face key points of the registered 3DMM model to obtain head posture information of the texture image; and on the basis of whether the head posture information meets preset requirements or not, determining whether to use the texture image for mapping. According to the technical solution, a standard 3DMM model is used to perform region division on a 3D grid model of a target object, and on the basis of head posture information of texture images, a texture image with the head posture meeting preset requirements is selected to perform mapping processing on a corresponding region, thereby making the texture of the mapping more consistent, and constructing a 3D face model having a better visual effect.

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 embodiment of the invention provides a character animation generation method and device based on voice driving, equipment and a medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring an audio signal input by a user, a target human portrait picture and a target scene reference video; encoding the audio signal to obtain an audio code; generating a three-dimensional face model motion coefficient according to the portrait picture of the target person and the audio code; extracting blink information and head posture information from the target scene reference video; correcting the motion coefficient of the three-dimensional face model according to the blinking information and the head posture information to obtain a corrected motion coefficient of the three-dimensional face model; and taking a three-dimensional deformable head model as a potential vector, mapping the three-dimensional deformable head model to the corrected three-dimensional face model motion coefficient frame by frame, converting the three-dimensional face model motion coefficient into a video frame, and generating a head speech video of the target person. According to the embodiment of the invention, the quality and reality of character animation generation driven by voice can be improved.

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.

In some embodiments, a computer-implemented method includes obtaining a without-eyeglasses face scan of a subject, the without-eyeglasses face scan being a three-dimensional (3D) model of a face of the subject without eyeglasses; obtaining a with-eyeglasses face scan of the subject, the with-eyeglasses face scan being a 3D model of the subject with eyeglasses; and using the without-eyeglasses face scan and the with-eyeglasses face scan to place a 3D eyeglasses model on a face model of the subject. In some embodiments of the computer-implemented method, the 3D eyeglasses model is placed on the face model of the subject using frame placement information generated using the without-eyeglasses face scan and the with-eyeglasses face scan.

The invention discloses an access control face recognition system and method based on a three-dimensional technology, and relates to the technical field of access control face recognition, and the recognition system comprises a face data acquisition module which is used for capturing three-dimensional face data of a target person by using a 3D camera; the multi-modal fusion module is used for fusing the three-dimensional face data and the traditional two-dimensional image data; the modeling module is used for generating an accurate three-dimensional face model; the dynamic adjustment module is used for creating a real-time dynamic adjustment model; the matching module is used for extracting facial features from the three-dimensional face model and matching the facial features with the three-dimensional face model in the database; and the identity verification and authorization module is used for carrying out identity verification according to the matching result. According to the method, three-dimensional face data and traditional two-dimensional image data are fused, the recognition accuracy and reliability can be improved, a real-time dynamic adjustment function is introduced, recognition parameters can be automatically optimized according to different environment light rays and angles, and better use prospects are brought.

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 invention discloses a multi-modal digital human generation method and system. The system comprises a picture generation digital human module, a video generation digital human module and a video translation module, relates to the technical field of deep synthesis, and solves the technical problem of low expansibility and flexibility of a digital human generation mode in the prior art. The method comprises the following steps: processing a reference image to generate a 3D face model; generating a first digital human from the motion coefficient and the first audio signal based on a face renderer and an appearance encoder; extracting an audio with a specified duration from the original video and marking the audio as an original audio; and converting the translated text into a corresponding target audio, and synthesizing the target audio and the original video into a translated video. According to the invention, the reference image is generated through the 3DMM technology to obtain the 3D face model, so that the 3D face model can maintain the identity characteristics of a person and generate a natural dynamic expression, and the interactive experience between the generated digital person and a user is 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 belongs to the field of artificial intelligence and financial science and technology, and relates to a human face three-dimensional reconstruction method and device based on artificial intelligence, computer equipment and a storage medium, and the method comprises the steps: carrying out the region-of-interest extraction of two-dimensional human face image data, obtaining a human face image, carrying out the key point detection of the human face image, and obtaining two-dimensional key points; performing face segmentation on the face image to obtain a face mask image; processing the face image based on the initial reconstruction model to obtain a three-dimensional face model; generating a first loss based on the three-dimensional face model and the face image; generating a second loss based on the three-dimensional face model, the two-dimensional key points and the face mask image; generating a comprehensive loss based on the first loss and the second loss; optimizing the initial reconstruction model based on the comprehensive loss to obtain a target reconstruction model; and performing three-dimensional reconstruction processing on the face image based on the target reconstruction model to obtain a target three-dimensional face model. According to the invention, the precision of three-dimensional face reconstruction is improved based on the use of the target reconstruction model.

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 invention provides a face model reconstruction method and device, equipment and a medium. The method comprises the following steps: acquiring multi-frame original point clouds of a user face; determining an original point cloud of a reference frame and an original point cloud of a non-reference frame in the multiple frames of original point clouds; according to the multiple frames of original point clouds, reconstructing a first face model matched with the expression state corresponding to the original point clouds of the reference frame; and reconstructing a second face model matched with the expression state corresponding to the original point cloud of each non-reference frame by aligning the first face model to the original point cloud of each non-reference frame. By using the method, the reconstruction efficiency of the dynamic face model can be improved.

The invention belongs to the field of 3D display, and provides a dynamic eyeball tracking method for naked eye 3D and a tablet personal computer, and the method comprises the steps: obtaining a video frame shot by the tablet personal computer, and carrying out the detection of eye and nose regions of the video frame based on an error learning framework; performing content classification on the eye and nose areas, extracting SIFT features in the eye and nose areas based on a scale invariant feature transform algorithm, and selecting a corresponding supervised descent model according to a content classification result to obtain first coordinates of pupil centers of human eyes; estimating a three-dimensional coordinate of the pupil center according to the 3D face model and the first coordinate, and dynamically adjusting a pixel arrangement mode of a screen of the tablet computer and the position of a cylindrical lens grating based on the three-dimensional coordinate, so that a left eye image and a right eye image projected by the tablet computer are matched with the three-dimensional coordinate of the pupil center; according to the invention, the problem that the naked-eye 3D effect cannot be achieved when the existing naked-eye 3D screen generates ghosting when the position of an observer is not correct enough or moves or the deviation exceeds a certain degree can be solved.

One variation of a method for securing synthetic video conference feeds includes, during a setup period: accessing a target image depicting a face of a user; generating a face model based on a target set of facial features detected in the target image; and linking the face model to a target set of facial biometric values of the user. The method also includes, during an operating period succeeding the setup period: accessing a frame; deriving characteristics of a set of facial features detected in the frame; extracting a set of facial biometric values from the frame; in response to alignment between the target set of facial biometric values and the set of facial biometric values, generating a synthetic face image based on characteristics of the set of facial features, the face model, and a synthetic face generator; and rendering the synthetic face image in place of the frame.

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.

Various embodiments of an apparatus, method(s), system(s) and computer program product(s) described herein are directed to a Viseme Engine. The Viseme Engine receives audio data associated with a user account. The Viseme Engine predicts at least one viseme that corresponds with a portion of phoneme audio data and identifies one or more facial expression parameters associated with the predicted viseme. The facial expression parameters being applicable to a face model. The Viseme Engine renders the predicted viseme according to the one or more facial expression parameters.

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 digital human modeling method, a digital human modeling device, digital human modeling equipment, a storage medium and a program product, relates to the technical field of human modeling, and provides a digital human modeling method based on multi-modal driving Gaussian sputtering. The method comprises the following steps: firstly, performing fuzzy reconstruction on a human face recorded in a real-time recorded video through three-dimensional geometric features extracted from the real-time recorded video to obtain a static three-dimensional human face model; then, dynamic deformation processing is carried out on the static three-dimensional face model through multi-modal features extracted from a real-time recorded video, and a dynamic three-dimensional model is obtained; therefore, the dynamic characteristics of the figure can be accurately captured and reproduced, the real and real-time digital person is obtained, and the expressive force and interactivity of the digital person in various application scenes are improved.

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.