Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Haozhi Huang

Yan Zhang

Xiaolong Zhu

Lin Ma

Jingmin Luo

Li Qin Shen

Hao Wang

Yonggen Ling

Wenhan Luo

Yitong Wang

Peng Yang

Wenhao Jiang

Lijian Mei

Shenghui Huang

Kaining Huang

Yongsen Zheng

Dong Yu

Jiawei Li

Shimin Hu

Chun Yuan

Senzhe Xu

Tianyu Sun

Xinyu Gong

Yugang Jiang

Minjun Li

Kun Cheng

A model training method includes obtaining an image sample set and brief-prompt information; generating a content mask set according to the image sample set and the brief-prompt information; generating a to-be-trained image set according to the content mask set; obtaining, based on the image sample set and the to-be-trained image set, a predicted image set through a to-be-trained information synthesis model, the predicted image set comprising at least one predicted image, the predicted image being in correspondence to the image sample; and training, based on the predicted image set and the image sample set, the to-be-trained information synthesis model by using a target loss function, to obtain an information synthesis model.

Model training method, media information synthesis method, and related apparatuses

An image processing method is provided. The method includes: encoding an input image based on an attention mechanism to obtain an encoding tensor set and an attention map set of the input image; obtaining an encoding result of the input image according to the encoding tensor set and the attention map set, the encoding result of the input image recording an identity feature of a human face in the input image; encoding an expression image to obtain an encoding result of the expression image, the encoding result of the expression image recording an expression feature of a human face in the expression image; and generating an output image according to the encoding result of the input image and the encoding result of the expression image, the output image having the identity feature of the input image and the expression feature of the expression image.

Image processing method and apparatus, device, and storage medium

A video sound-picture matching includes: acquiring a voice sequence; acquiring a voice segment from the voice sequence; acquiring an initial position of a start-stop mark and a moving direction of the start-stop mark from an image sequence; determining an active segment according to the initial position of the start-stop mark, the moving direction of the start-stop mark, and the voice segment; and synthesizing the voice segment and the active segment to obtain a video segment. In a video synthesizing process, the present disclosure uses start-stop marks to locate positions of active segments in an image sequence, so as to match the active segments having actions with voice segments, so that the synthesized video segments are more in line with natural laws of a character during speaking, and have better authenticity.

Sound-picture matching method of video, related apparatus, and storage medium

Methods, devices, and storage medium for fusing at least one image are disclosed. The method includes obtaining a first to-be-fused image and a second to-be-fused image, the first to-be-fused image comprising first regions, and the second to-be-fused image comprising second regions; obtaining a first feature set according to the first to-be-fused image and obtaining a second feature set according to the second to-be-fused image; performing first fusion processing on the first to-be-fused image and the second to-be-fused image by using a shape fusion network model to obtain a third to-be-fused image, the third to-be-fused image comprising at least one first encoding feature and at least one second encoding feature; and performing second fusion processing on the third to-be-fused image and the first to-be-fused image by using a condition fusion network model to obtain a target fused image. Model training methods, apparatus, and storage medium are also disclosed.

Image fusion method, model training method, and related apparatuses

A method, apparatus, and storage medium for training a neural network model used for image processing are described. The method includes: obtaining a plurality of video frames; inputting the plurality of video frames through a neural network model so that the neural network model outputs intermediate images; obtaining optical flow information between an early video frame and a later video frame; modifying an intermediate image corresponding to the early video frame according to the optical flow information to obtain an expected-intermediate image; determining a time loss between an intermediate image corresponding to the later video frame and the expected-intermediate image; determining a feature loss between the intermediate images and a target feature image; and training the neural network model according to the time loss and the feature loss, and returning to obtaining a plurality of video frames continue training until the neural network model satisfies a training finishing condition.

Method and apparatus for training neural network model used for image processing, and storage medium

In a multi-person pose recognition method, a to-be-recognized image is obtained, and a circuitous pyramid network is constructed. The circuitous network pyramid includes parallel phases, and each phase includes downsampling network layers, upsampling network layers, and a first residual connection layer to connect the downsampling and upsampling network layers. The phases are interconnected by a second residual connection layer. The circuitous pyramid network is traversed, by extracting a feature map for each phase, and the feature map of the last phase is determined to be the feature map of the to-be-recognized image. Multi-pose recognition is then performed on the to-be-recognized image according to the feature map to obtain a pose recognition result for the to-be-recognized image.

Multi-person pose recognition method and apparatus, electronic device, and storage medium

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

Embodiments of this application disclose methods, systems, and devices for video synthesis. In one aspect, a method comprises obtaining a plurality of frames corresponding to source image information of a first to-be-synthesized video, each frame of the source image information. The method also comprises obtaining a plurality of frames corresponding to target image information of a second to-be-synthesized video. For each frame of the plurality of frames corresponding to the target image information of the second to-be-synthesized video, the method comprises fusing a respective source image from the first to-be-synthesized video, a corresponding source motion key point, and a respective target motion key point corresponding to the frame using a pre-trained video synthesis model, and generating a respective output image in accordance with the fusing. The method further comprises repeating the fusing and the generating steps for the second to-be-synthesized video to produce a synthesized video.

Video synthesis method, model training method, device, and storage medium

An image processing method for a computer device. The method includes obtaining a to-be-processed image belonging to a first image category; inputting the to-be-processed image into a first stage image conversion model, to obtain a first intermediate image; and converting the first intermediate image into a second intermediate image through a second stage image conversion model. The method also includes determining a first weight matrix corresponding to the first intermediate image; determining a second weight matrix corresponding to the second intermediate image; and fusing the first intermediate image and the second intermediate image according to the corresponding first weight matrix and second weight matrix, to obtain a target image corresponding to the to-be-processed image and belonging to a second image category. A sum of the first weight matrix and the second weight matrix being a preset matrix.

Image processing method, storage medium, and computer device

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.

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Shenzhen, China

Haozhi Huang