Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Xing Ji

Yitong Wang

Linchao Bao

Sheng Wang

Zheng Zhou

Xiangkai Lin

Zhantu Zhu

Peng Liu

Yi Tong Wang

Hao Wang

Xiaolong Zhu

Min Luo

Zhifeng Li

Shiyin Kang

Deyi Tuo

Kuongchi Lei

Fan Jia

Wei Dong Chen

Jun Huang

Jie Fan

Jingmin Luo

Lijian Mei

Yuan Fang

Shenghui Huang

Xiao Long Zhu

Ren Jian Chen

Wanyu Huang

Shao Bin Lin

This application discloses an artificial intelligence (AI) based animation character drive method. A first expression base of a first animation character corresponding to a speaker is determined by acquiring media data including a facial expression change when the speaker says a speech, and the first expression base may reflect different expressions of the first animation character. After target text information is obtained, an acoustic feature and a target expression parameter corresponding to the target text information are determined according to the target text information, the foregoing acquired media data, and the first expression base. A second animation character having a second expression base may be driven according to the acoustic feature and the target expression parameter, so that the second animation character may simulate the speaker's sound and facial expression when saying the target text information, thereby improving experience of interaction between the user and the animation character.

Artificial intelligence-based animation character drive method and related apparatus

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

Embodiments of this application disclose an artificial intelligence-based (AI-based) animation character control method. When one animation character has a corresponding face customization base, and one animation character has no corresponding face customization base, the animation character having the face customization base may be used as a driving character, and the animation character having no face customization base may be used as a driven character. The method may include: obtaining an expression base corresponding to the driving character and an expression base corresponding to the driven character; determining a mapping relationship between expression parameters according to the expression base corresponding to the driving character and the expression base corresponding to the driven character; and driving the driven character according to the mapping relationship by using the known expression parameter of the driving character and the foregoing mapping relationship, so that the driven character is driven to make an actual expression identified by the known expression parameter. Therefore, a new animation character may be directly driven by using a known expression parameter of another animation character without performing processing to obtain a face customization base corresponding to the new animation character, thereby increasing the speed of launching the new animation character.

Artificial intelligence-based animation character control and drive method and apparatus

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

A face key point detection method includes determining, according to an image containing a face, a multi-channel feature map of the image; converting the multi-channel feature map into a predetermined channel quantity of target feature maps; performing a convolution operation on each target feature map in the predetermined channel quantity of target feature maps by using a convolution kernel corresponding to each target feature map; generating a feature vector corresponding to the image based on a result of the convolution operation on each target feature map; and determining key point coordinates of the face on the image according to the feature vector.

Face key point detection method and apparatus, storage medium, and electronic device

A video editing method, apparatus and storage medium are provided. The method includes obtaining an object, the object including one or more images; determining a content element of the object for video editing, the content element having a content type identifier; determining a material set identifier corresponding to the content type identifier according to a first behavior tree logic; determining a video editing material set corresponding to the material set identifier; and obtaining an edited video according to the content element and the video editing material set.

Video editing method, apparatus, and device, and storage medium

This application disclose an artificial intelligence (AI) based animation character drive method. A first expression base of a first animation character corresponding to a speaker is determined by acquiring media data including a facial expression change when the speaker says a speech, and the first expression base may reflect different expressions of the first animation character. After target text information is obtained, an acoustic feature and a target expression parameter corresponding to the target text information are determined according to the target text information, the foregoing acquired media data, and the first expression base. A second animation character having a second expression base may be driven according to the acoustic feature and the target expression parameter, so that the second animation character may simulate the speaker's sound and facial expression when saying the target text information, thereby improving experience of interaction between the user and the animation character.

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 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.

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Shenzhen, China

Xing Ji