Tencent Technology (Shenzhen) Company Limited

Tencent Technology Company Limited

Ying Tai

Chengjie Wang

Jilin Li

Keke He

Junwei Zhu

Feiyue Huang

Yun Cao

Yabiao Wang

Yang Yi

Xiaoming Yu

Juanhui Tu

Jinlong Peng

Yi Zhou

Zhengkai Jiang

Hui Ni

Shouhong Ding

Chenghao Liu

Yuchen Luo

Wenqing Chu

Xiaozhong Ji

Feida Zhu

Xu Chen

Jian Zhang

Yanhao Ge

Shaoxin Li

Yandan Zhao

Jia Meng

Taiping Yao

Xinyi Zhang

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

An de-identification method includes: performing sampling on a target space of a projector in a target network model to obtain N virtual identity vectors, N being a positive integer; extracting, by using an attribute extractor in the target network model, M attribute vectors from a target image, M being a positive integer; and generating, by using a generator in the target network model, a de-identified image of the target image based on the N virtual identity vectors and the M attribute vectors.

Model training method and apparatus, deidentification method and apparatus, device, and storage medium

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

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

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

This application provides a face living body detection method performed by a computing device, the method including: obtaining a first face image of a target detection object in a first illumination condition and a second face image of the target detection object in a second illumination condition, determining a difference image according to the two images, decoupling an object reflectivity and an object normal vector corresponding to the target detection object from a feature map extracted from the difference image, and determining whether the target detection object is a living body according to the object reflectivity and the object normal vector. This method decouples texture information and depth information of a face, and performs living body detection by using decoupled information, which increases the defense capability against 3D attacks, thereby effectively defending against planar attacks and 3D attacks.

Face living body detection method and apparatus, device, and storage medium

This application relates to an image processing method and apparatus, a facial recognition method and apparatus, a computer device, and a readable storage medium. The image processing method includes: obtaining a target image comprising an object wearing glasses; inputting the target image to a glasses-removing model comprising a plurality of sequentially connected convolution squeeze and excitation networks; obtaining feature maps of feature channels of the target image through convolution layers of the convolution squeeze and excitation networks; obtaining global information of the feature channels according to the feature maps through squeeze and excitation layers of the convolution squeeze and excitation networks, learning the global information, and generating weights of the feature channels; weighting the feature maps of the feature channels according to the weights through weighting layers of the convolution squeeze and excitation networks, respectively, and generating weighted feature maps; and generating a glasses-removed image corresponding to the target image according to the weighted feature maps through the glasses-removing model. The glasses in the image can be effectively removed using the method.

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Shenzhen, China

Ying Tai