Tencent Technology (shenzhen) Company Limited

Comp-any Gmbh

Tencent Technology (shenzhen)

Tencent Technology Company Limited

Chengjie Wang

Jilin Li

Feiyue Huang

Yongjian Wu

Yanhao Ge

Hui Ni

Ying Tai

Yabiao Wang

Keke He

Shouhong Ding

Yun Cao

Yandan Zhao

Guofu Tan

Junwei Zhu

Shaoxin Li

Yang Yi

Xiaoming Yu

Juanhui Tu

Yuan Huang

Chenghao Liu

Xiaoming Huang

Lei Zhang

Jian Tong Li

Bin Zhang

Jing Liu

Jinlong Peng

Yicong Liang

Xuan Cao

Weijian Cao

Yi Zhou

Zhengkai Jiang

Ling Zhao

Weiming Dong

Yichao Yan

Xiaozhong Ji

Feida Zhu

Zhenye Gan

Kekai Sheng

Jing Jing Liu

Bin Fu

Jia Meng

Ruixin Zhang

Taiping Yao

Pengcheng Shen

Xu Chen

Yirong Zeng

Xueyuan Xing

Donghao Luo

Changyou Deng

Yafeng Zhao

Xiaolong Liang

Changwei He

Yuchen Luo

Wenqing Chu

Xinyi Zhang

Yichao Xiong

Shuheng Lin

Weijan Cao

Pan Cheng

Chenyang Guo

Boyuan Deng

Siqian Yang

Boyuan Jiang

Mingyu Wu

This application provides an image processing method performed by a computer device. The image processing method includes: receiving a face swapping request; acquiring an attribute parameter of the image, an attribute parameter of the target face, and a face feature of the target face, the attribute parameter of the image indicating a three-dimensional attribute of the face in the image; determining a target attribute parameter based on the attribute parameter of the image and the attribute parameter of the target face; determining a target comprehensive feature based on the target attribute parameter and the face feature of the target face; encoding the image to obtain an image encoding feature of the image; migrating the target comprehensive feature to the image encoding feature of the image by normalization to obtain a fusion encoding feature; and decoding the fusion encoding feature to obtain a target face-swapped image including a fusion face.

Image processing method and apparatus, computer device, computer-readable storage medium, and computer 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

A facial image processing method includes: obtaining first optical flow information; obtaining, based on the first optical flow information, second optical flow information through an optical flow information prediction sub-model of an image processing model; generating, based on the first facial sample image and the second optical flow information, a predicted image of the first facial sample image through a generator of the image processing model; discriminating the predicted image and the second facial sample image through a discriminator of the image processing model to obtain a first discrimination result and a second discrimination result; and training the image processing model based on the first discrimination result, the second discrimination result, the predicted image, the second facial sample image, the first optical flow information, and the second optical flow information.

Facial image processing method and apparatus, device, and storage medium

This application provides an image processing method and apparatus. The method includes acquiring an identity feature of a source image and an initial attribute feature of at least one measure of a target image in response to receiving a face change request; inputting the identity feature and the initial attribute feature of the at least one measure into a face change model; iteratively performing feature fusion on the identity feature and the initial attribute feature of the at least one measure by using the face change model to obtain a fusion feature; and generating a target face change image based on the fusion feature by using the face change model, and outputting the target face change image, a face in the target face change image being fused with an identity feature of the source face and a target attribute feature of the target face.

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

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

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

This application provides a method for reconstructing a three-dimensional model, a method for training a three-dimensional reconstruction model, an apparatus, a computer device, and a storage medium. The method for reconstructing a three-dimensional model includes: obtaining an image feature coefficient of an input image; respectively obtaining, according to the image feature coefficient, a global feature map and an initial local feature map based on a texture and a shape of the input image; performing edge smoothing on the initial local feature map, to obtain a target local feature map; respectively splicing the global feature map and the target local feature map based on the texture and the shape, to obtain a target texture image and a target shape image; and performing three-dimensional model reconstruction according to the target texture image and the target shape image, to obtain a target three-dimensional model.

Method for reconstructing three-dimensional model, method for training three-dimensional reconstruction model, and apparatus

A method, an apparatus, and a device for image processing and a training method thereof are provided. The training method includes obtaining a sample image set, the sample image set comprising a first number of sample images; constructing an image feature set based on the sample image set, the image feature set comprising an image feature extracted from each of the sample images in the sample image set; obtaining a training image set, the training image set comprising a second number of training images; constructing multiple training image pairs based on the training image set and the image feature set; and training the image processing model based on the multiple training image pairs.

Training method of image processing model, image processing method, apparatus, and device

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 image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

Face image processing method and apparatus, face image display method and apparatus, and device

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

A target tracking method is provided for a computer device. The method includes determining a target candidate region of a current image frame; capturing a target candidate image matching the target candidate region from the current image frame; determining a target region of the current image frame according to an image feature of the target candidate image; determining motion prediction data of a next image frame relative to the current image frame by using a motion prediction model and according to the image feature of the target candidate image; and determining a target candidate region of the next image frame according to the target region and the motion prediction data.

Target tracking method, computer device, and storage medium

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 application relates to a method and an apparatus for detecting facial key points, a computer device, and a storage medium including: acquiring a to-be-detected face image from a current frame; determining partial images in the to-be-detected face image, each partial image including one or more facial key points; determining, within each of the partial images, candidate points of the one or more facial key points corresponding to the partial image, respectively; and jointly constraining the candidate points in the partial images to determine a set of facial key points from the candidate points for the to-be-detected face image. For the partial images in the entire to-be-detected face image, the candidate points of the facial key points corresponding to the partial images are respectively determined. Therefore, a calculation amount may be reduced, and the efficiency of determining the candidate points of the facial key points is improved.

Method and apparatus for detecting facial key points, computer device, and storage medium

Growing community of inventors

Shenzhen, China

Chengjie Wang