Tencent Technology (shenzhen) Company Limited

Tsinghua University

Tencent Technology Company Limited

Feiyue Huang

Jilin Li

Yongjian Wu

Chengjie Wang

Yabiao Wang

Ke Li

Shouhong Ding

Guofu Tan

Keke He

Wei Li

Hui Ni

Ying Tai

Binghua Qian

Fuzhang Wu

Xingming Jin

Xiaowei Guo

Yanhao Ge

Yandan Zhao

Yang Yi

Juanhui Tu

Ruixin Zhang

Xiaoming Yu

Hui Wang

Dan Wu

Yuan Huang

Cong Yao

Shaoxin Li

Chenghao Liu

Xiaoming Huang

Junwei Zhu

Lei Zhang

Jian Tong Li

Bin Zhang

Xu Chen

Feng Gao

Bin Hua Liu

Lu Zhang

Yang Chen

Yang He

Juhong Wang

Bin Liu

Jinlong Peng

Yi Zhou

Zhengkai Jiang

Xiaoli Jiang

Jianguo Xie

Weiming Dong

Fangmai Zheng

Xiang Bai

Baoguang Shi

Bilei Zhu

Zhenye Gan

Junwu Chen

Yihui Liu

Jiandong Xie

Kekai Sheng

Yun Cao

Yunsheng Wu

Shimin Hu

Taiping Yao

Kun Xu

Zhibo Chen

Ling Zhao

Yicong Liang

Lei Shen

Liqian Dong

Shang Wu

Yuge Huang

Yichao Yan

Xiaoqing Liang

Ke Mei

Chengzhao Zhang

Zongqiao Yu

Qingliang Lin

Changyou Deng

Yafeng Zhao

Xiaolong Liang

Hangyu Yan

Donghao Luo

Yingyi Zhang

Siqian Yang

Shen Chen

Chenyang Guo

Boyuan Deng

Boyuan Jiang

Mingyu Wu

Zhiwei Tao

Kejing Zhou

Changwei He

Xiaoguang Yu

Yichao Xiong

Shuguang Huang

This application discloses a face image processing method performed by an electronic device. The method includes: acquiring a face image of a source face and a face template image of a template face; performing three-dimensional face modeling on the face image and the face template image to obtain a three-dimensional face image feature of the face image and a three-dimensional face template image feature of the face template image; fusing the three-dimensional face image feature and the three-dimensional face template image feature to obtain a three-dimensional fusion feature; performing face replacement feature extraction on the face image based on the face template image to obtain an initial face replacement feature; transforming the initial face replacement feature based on the three-dimensional fusion feature to obtain a target face replacement feature; and replacing the template face with the source face based on the target face replacement feature to obtain a target face image.

Face image processing method, face image processing model training method, apparatus, device, storage medium, and program product

In the field of artificial intelligence technologies, a gaze correction method and apparatus for a face image, a device, a computer-readable storage medium, and a computer program product are provided. The method includes: acquiring an eye image from a face image; determining an eye movement flow field based on the eye image and a target gaze direction, the target gaze direction being a gaze direction to which an eye gaze in the eye image is to be corrected; adjusting a pixel position in the eye image based on the eye movement flow field, to obtain a corrected eye image; and generating a face image with a corrected gaze based on the corrected eye image.

Gaze correction method and apparatus for face image, device, computer-readable storage medium, and computer program product face image

An image detection method includes obtaining a facial image, and obtaining a frequency-domain image of the facial image and a spatial-domain feature of the facial image, the frequency-domain image being obtained by performing frequency-domain transformation on the facial image. The spatial-domain feature is obtained by performing feature extraction on the facial image. The method further includes performing feature extraction based on the frequency-domain image, to obtain a frequency-domain feature of the facial image, and fusing the spatial-domain feature and the frequency-domain feature by using an attention fusion network of a facial image detection model, to obtain a fused feature of the facial image. The method further includes obtaining a detection result of the facial image based on the fused feature, the detection result indicating whether the facial image is a forged facial image.

Detecting forged facial images using frequency domain information and local correlation

The present subject matter discloses an action recognition method, apparatus and device, a storage medium, and a computer program product, belonging to the field of image recognition. Multiple video frames in a target video are obtained. Feature extraction is performed on the multiple video frames respectively according to multiple dimensions to obtain multiple multi-channel feature patterns. Each video frame corresponds to one multi-channel feature pattern. Each channel represents one dimension. An attention weight of each multi-channel feature pattern is determined based on a similarity between every two multi-channel feature patterns. The attention weight is used for representing a degree of correlation between a corresponding multi-channel feature pattern and an action performed by an object in the target video. A type of the action is determined based on the multiple multi-channel feature patterns and the determined multiple attention weights.

Action recognition method, apparatus and device, storage medium and computer program product

An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product. The image gaze correction method includes: acquiring a to-be-corrected eye image from a to-be-corrected image, generating, based on the to-be-corrected eye image, an eye motion flow field and an eye contour mask, the eye motion flow field being used for adjusting a pixel position in the to-be-corrected eye image, and the eye contour mask being used for indicating a probability that the pixel position in the to-be-corrected eye image belongs to an eye region, performing, based on the eye motion flow field and the eye contour mask, gaze correction processing on the to-be-corrected eye image to obtain a corrected eye image, and generating a gaze corrected image based on the corrected eye image.

Image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product

Embodiments of this application disclose a palm print recognition method, a method for training a feature extraction model, a device, and a storage medium, and belong to the field of computer technologies. The method includes: obtaining sample hand images for each of a plurality of sample user identifiers, at least two of the sample hand images associated with the each of the plurality of sample user identifier being acquired from image acquisition devices having different image acquisition characteristics; calling a feature extraction model to extract sample palm print features of the plurality of sample user identifiers; and adjusting the feature extraction model according to the sample palm print features of the plurality of sample user identifiers.

Palm print recognition method, method for training feature extraction model, comprising obtaining sample hand images for user identifiers, calling feature extraction model to extract palm print features, and adjust the feature extraction model according to the sample palm print features

An image processing method includes performing additional image feature extraction on a training source face image to obtain a source additional image feature, performing identity feature extraction on the training source face image to obtain a source identity feature, inputting a training template face image into an encoder in a to-be-trained face swapping model to obtain a face attribute feature, inputting the source additional image feature, the source identity feature, and the face attribute feature into a decoder in the face swapping model for decoding to obtain a decoded face image, obtaining a target model loss value based on an additional image difference between the decoded face image and a comparative face image, and adjusting the model parameters of the encoder and the decoder based on the target model loss value to obtain the trained face swapping model.

Image processing method and apparatus, computer device, storage medium, and computer program product

An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product related to the field of artificial intelligence technologies are provided. The image gaze correction method includes: acquiring an eye image from an image; performing feature extraction processing on the eye image to obtain feature information of the eye image; performing, based on the feature information and a target gaze direction, gaze correction processing on the eye image to obtain an initially corrected eye image and an eye contour mask; performing, by using the eye contour mask, adjustment processing on the initially corrected eye image to obtain a corrected eye image; and generating a gaze corrected image based on the corrected eye image.

A face detection method includes: acquiring a target image; invoking a face detection network, and processing the target image by using a feature extraction structure of the face detection network, to obtain original feature maps corresponding to the target image; the original feature maps having different resolutions; processing the original feature maps by using a feature enhancement structure of the face detection network, to obtain an enhanced feature map corresponding to each original feature map; the feature enhancement structure being obtained by searching a search space, and the search space used for searching the feature enhancement structure being determined based on a detection objective of the face detection network and a processing object of the feature enhancement structure; and processing the enhanced feature map by using a detection structure of the face detection network, to obtain a face detection result of the target image.

Face detection method, apparatus, and device, and training method, apparatus, and device for image detection neural network

An action recognition method includes: obtaining original feature submaps of each of temporal frames on a plurality of convolutional channels by using a multi-channel convolutional layer; calculating, by using each of the temporal frames as a target temporal frame, motion information weights of the target temporal frame on the convolutional channels according to original feature submaps of the target temporal frame and original feature submaps of a next temporal frame, and obtaining motion information feature maps of the target temporal frame on the convolutional channels according to the motion information weights; performing temporal convolution on the motion information feature maps of the target temporal frame to obtain temporal motion feature maps of the target temporal frame; and recognizing an action type of a moving object in image data of the target temporal frame according to the temporal motion feature maps of the target temporal frame on the convolutional channels.

Action recognition method and apparatus, computer storage medium, and computer device

This present disclosure describes a video image processing method and apparatus, a computer-readable medium and an electronic device, relating to the field of image processing technologies. The method includes determining, by a device, a target-object region in a current frame in a video. The device includes a memory storing instructions and a processor in communication with the memory. The method also includes determining, by the device, a target-object tracking image in a next frame and corresponding to the target-object region; and sequentially performing, by the device, a plurality of sets of convolution processing on the target-object tracking image to determine a target-object region in the next frame. A quantity of convolutions of a first set of convolution processing in the plurality of sets of convolution processing is less than a quantity of convolutions of any other set of convolution processing.

Method, apparatus, and storage medium for processing video image

A melody information processing method is described. A piece of Musical Instrument Digital Interface (MIDI) data corresponding to a song is received, a song identifier of the song is obtained, first melody information is generated according to the MIDI data, and the first melody information is stored in association with the song identifier in a melody database. Moreover, a user unaccompanied-singing audio data set that is uploaded from a user terminal is received, second melody information corresponding to the song identifier is extracted according to the user unaccompanied-singing audio data set, and the second melody information is stored in association with the song identifier in the melody database.

Method, server, and storage medium for melody information processing

A statistical parameter modeling method is performed by a server. After obtaining model training data, the model training data including a text feature sequence and a corresponding original speech sample sequence, the server inputs an original vector matrix formed by matching a text feature sample point in the text feature sample sequence with a speech sample point in the original speech sample sequence into a statistical parameter model for training and then performs non-linear mapping calculation on the original vector matrix in a hidden layer, to output a corresponding prediction speech sample point. The server then obtains a model parameter of the statistical parameter model according to the prediction speech sample point and a corresponding original speech sample point by using a smallest difference principle, to obtain a corresponding target statistical parameter model.

Statistical parameter model establishing method, speech synthesis method, server and storage medium

This application discloses a human attribute recognition method performed at a computing device. The method includes: determining a human body region image in a surveillance image; inputting the human body region image into a multi-attribute convolutional neural network model, to obtain, for each of a plurality of human attributes in the human body region image, a probability that the human attribute corresponds to a respective predefined attribute value, the multi-attribute convolutional neural network model being obtained by performing multi-attribute recognition and training on a set of pre-obtained training images by using a multi-attribute convolutional neural network; determining, for each of the plurality of human attributes in the human body region image, the attribute value of the human attribute based on the corresponding probability; and displaying the attribute values of the plurality of human attributes next to the human body region image.

Human body attribute recognition method, apparatus, and device and medium

A trajectory tracking method is provided for a computer device. The method includes performing motion tracking on head images in a plurality of video frames, to obtain motion trajectories corresponding to the head images; acquiring face images corresponding to the head images in the video frames, to obtain face image sets corresponding to the head images; determining from the face image sets corresponding to the head images, at least two face image sets having same face images; and combining motion trajectories corresponding to the at least two face image sets having same face images, to obtain a final motion trajectory of trajectory tracking.

Trajectory tracking method and apparatus, computer device, and storage medium

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Shenzhen, China

Feiyue Huang