Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Yitong Wang

Xiaolong Zhu

Xing Ji

Jingmin Luo

Lijian Mei

Shenghui Huang

Kaining Huang

Hao Wang

Zheng Zhou

Haozhi Huang

Zhifeng Li

Yongsen Zheng

Fan Jia

Yan Zhang

Jun Huang

Linchao Bao

Yonggen Ling

Min Luo

Mu Hu

Sirui Gao

An electronic device obtains an image that includes a face. The device performs feature extraction on the image, to obtain facial expression information corresponding to the face and facial feature information corresponding to the facial expression, wherein the facial feature information indicates an extent of the facial expression. The device determines facial emotion information according to the facial expression information. The device also determines facial feature expression information according to a target feature value corresponding to the facial emotion and the facial feature information. This expression recognition techniques disclosed herein can implement multi-task learning and reduce an amount of data required for model training, and can obtain both an emotion recognition result and a local expression recognition result, thereby improving efficiency and real-time performance of expression recognition and improving user experience.

Expression recognition method and related apparatus

Embodiments of this application provide an object tracking method performed by a computer device. The method includes, when a target object is lost in a second image frame in a first subsequent image frames, determining, according to a first local feature and in second subsequent image frames starting with the second image frame, a third image frame in which the target object reappears after the target object is lost during the tracking; determining a location of a target object region in the third image frame including the target object; and continuing to track the target object in image frames according to the location of the target object region in the third image frame. Through the object tracking method, a lost object can be detected and repositioned by using an extracted first local feature of the target object, thereby effectively resolving the problem in the existing technical solution.

Object tracking method, tracking processing method, corresponding apparatus, and electronic device

This application provides a method for training a pose recognition model performed at a computer device. The method includes: inputting a sample image labeled with human body key points into a feature map model included in a pose recognition model, to output a feature map of the sample image; inputting the feature map into a two-dimensional (2D) model included in the pose recognition model, to output 2D key point parameters used for representing a 2D human body pose; input a target human body feature map cropped from the feature map and the 2D key point parameter into a three-dimensional (3D) model included in the pose recognition model, to output 3D pose parameters used for representing a 3D human body pose; constructing a target loss function based on the 2D key point parameters and the 3D pose parameters; and updating the pose recognition model based on the target loss function.

Method and apparatus for training pose recognition model, and method and apparatus for image recognition

This application discloses a method for training a gaze tracking model, including: obtaining a training sample set; processing the eye sample images in the training sample set by using an initial gaze tracking model to obtain a predicted gaze vector of each eye sample image; determining a model loss according to a cosine distance between the predicted gaze vector and the labeled gaze vector for each eye sample image; and iteratively adjusting one or more reference parameters of the initial gaze tracking model until the model loss meets a convergence condition, to obtain a target gaze tracking model. According to the solution provided in this application, a gaze tracking procedure is simplified, a difference between a predicted value and a labeled value can be better represented by using the cosine distance as a model loss to train a model, to improve prediction accuracy of the gaze tracking model.

Method and apparatus for training gaze tracking model, and method and apparatus for gaze tracking

An object detection training method can include receiving a training sample set in a current iteration of an object detection training process over an object detection neural network. The training sample set can include first samples of a first class and second samples of a second class. A first center loss value of each of the first and second samples can be determined. The first center loss value can be a distance between a feature vector of the respective sample and a center feature vector of the first or second class which the respective sample belongs to. A second center loss value of the training sample set can be determined according to the first center loss values of the first and second samples. A first target loss value of the current iteration can be determined according to the second center loss value of the training sample set.

Face detection training method and apparatus, and electronic device

An image recognition method is provided. The method includes obtaining predicted locations of joints of a target person in a to-be-recognized image based on a joint prediction model, where the joint prediction model is pre-constructed by: obtaining a plurality of sample images; inputting training features of the sample images and a body model feature to a neural network and obtaining predicted locations of joints in the sample images outputted by the neural network; updating a body extraction parameter and an alignment parameter; and inputting the training features of the sample images and the body model feature to the neural network to obtain the joint prediction model.

Image recognition method and apparatus, electronic device, and readable storage medium using an update on body extraction parameter and alignment parameter

This application provides a method for configuring parameters of a three-dimensional face model. The method includes: obtaining a reference face image; identifying a key facial point on the reference face image to obtain key point coordinates as reference coordinates; and determining a recommended parameter set in a face parameter value space according to the reference coordinates. The first projected coordinates are projected coordinates of the key facial point obtained by projecting a three-dimensional face model corresponding to the recommended parameter set onto a coordinate system. The proximity of the first projected coordinates to the reference coordinates meets a preset condition.

Parameter configuration method, apparatus, and device for three-dimensional face model, and storage medium

An image processing method and a related apparatus are provided. The method is applied to an image processing device, and includes: obtaining an original image, the original image including a foreground object; extracting a foreground region from the original image through a deep neural network; identifying pixels of the foreground object from the foreground region; forming a mask according to the pixels of the foreground object, the mask including mask values corresponding to the pixels of the foreground object; and extracting the foreground object from the original image according to the mask.

Image processing method and apparatus

An object detection training method can include receiving a training sample set in a current iteration of an object detection training process over an object detection neural network. The training sample set can include first samples of a first class and second samples of a second class. A first center loss value of each of the first and second samples can be determined. The first center loss value can be a distance between an eigenvector of the respective sample and a center eigenvector of the first or second class which the respective sample belongs to. A second center loss value of the training sample set can be determined according to the first center loss values of the first and second samples. A first target loss value of the current iteration can be determined according to the second center loss value of the training sample set.

An augmented reality processing method is provided for a terminal. The method includes: obtaining a plurality of frames of images, comprising a first image and a second image, which is a frame of an image immediately following the first image; obtaining a key point set of a first object in the first image; obtaining, through a neural network model, first pose key point sets respectively corresponding to a plurality of objects in the second image; determining a second pose key point set of the first object in the second image according to the key point set and a motion trend of the first object; using a target first pose key point set as a key point set of the first object in the second image; and generating an augmented information image according to the key point set of the first object in the second image.

Augmented reality processing method, object recognition method, and related device

A video image processing method and apparatus is described. The method includes obtaining a first key point of an Mframe image in a video. The method further includes performing weighted smoothing on the first key point of the Mframe image according to first key points in a historical key point queue and weights in a first target weight queue, to obtain a target key point. The historical key point queue includes a first key point corresponds to each frame image in N frame images. The N frame images are images before the Mframe image, N&#x3e;0. The weights in the first target weight queue corresponding to the first key points in the historical key point queue, A weight corresponding to a first key point of an (M-a)frame image being greater than or equal to a weight corresponding to a first key point of an (M-b)frame image, and a&#x3c;b. The method further includes adjusting the Mframe image according to the target key point.

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Shenzhen, China

Yitong Wang