Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Bin Fu

Pei Cheng

Gang Yu

Qianyi Wang

Feng Shi

Yu Gao

Yunsheng Wu

Yanggang Dai

Mengren Qian

Keyi Shen

Lingrui Cui

Faqiang Wu

Mengxi Wang

Yuxuan Yan

Jingjin Zhou

Yirong Zeng

Qiang Li

Chao Wang

Chengjie Wang

Xiaolong Zhu

Rui Zhang

Yanhao Ge

Tao Xiong

Pengcheng Shen

Yang Ding

Xuan Qiu

Zhenhai Wu

Youcheng Ben

Xiaoqi Li

Xueyuan Xing

Lei Ying

Ruixue Shen

Yuan Hua

Zhaoting Zheng

Disclosed is a method for training an expression transfer model performed by a computer device, the method including: obtaining a source domain facial image of a first object, a target domain facial image of a second object and a facial feature image associated with the target domain facial image; applying the facial feature image and the source domain facial image to an expression transfer model to obtain a synthesized facial image of the first object; applying the synthesized facial image and the target domain facial image to a discriminative network model obtain two discrimination results; applying the synthesized facial image and the target domain facial image to an image classification model to obtain a category feature vector for identifying a difference between the synthesized facial image and the target domain facial image; and updating the expression transfer model according to the category feature vector and the discrimination results.

Training method for expression transfer model, expression transfer method and apparatus

An image processing method and apparatus, a device, and a storage medium. The image processing method includes: performing preliminary segmentation recognition on a raw image by using a first segmentation model to obtain a candidate foreground image region and a candidate background image region of the raw image; recombining the candidate foreground image region, the candidate background image region, and the raw image to obtain a recombined image, pixels in the recombined image being in a one-to-one correspondence with pixels in the raw image; and performing region segmentation recognition on the recombined image by using a second segmentation model to obtain a target foreground image region and a target background image region of the raw image.

Image processing method and apparatus, device and storage medium

A computing device obtains a to-be-optimized image. The device aligns the to-be-optimized image to obtain a to-be-optimized aligned image. The to-be-optimized aligned image includes a target region having points of objects that are distributed in a standard position. The computing device uses the to-be-optimized aligned image as an inputs to a generation network. The device performs feature extraction on the to-be-optimized aligned image using the generation network, to obtain an optimized image. The generation network is obtained by training a to-be-trained generative adversarial deep neural network model according to a low-quality image pair and a joint loss function. The low-quality image pair includes a target image and a low-quality image corresponding to the target image.

Image optimization method and apparatus, computer storage medium, and electronic device

Embodiments of this application disclose a video data processing method and apparatus, and a storage medium. After historical and current video frames are acquired from a video stream, two point position sets of a human body are obtained from the historical video frame and the current video frame, respectively. A jitter amplitude of a human body key point in the current video frame is analyzed according to the point position set of the human body in the current video frame and the point position set of the human body in the historical video frame; an adjustment reference position of the human body in the current video frame is determined according to the jitter amplitude; and subsequently a human body part below the adjustment reference position in the current video frame is adjusted and the adjusted current video frame is outputted for display as part of the video stream.

Video data processing method and apparatus, and storage medium

In a method for generating a cartoon face image that is performed by a computing device, a to-be-processed face image is obtained. Face feature information is recognized from the to-be-processed face image, and cartoon materials corresponding to feature parts of a face in the to-be-processed face image are obtained according to the face feature information. The obtained cartoon materials are cartoon face materials. The cartoon face materials include cartoon facial feature materials and another cartoon material other than the cartoon facial feature materials. Each of the cartoon facial feature materials are adjusted by using an adjustment parameter, and the other cartoon material are combined with the cartoon facial feature materials after adjustment to generate the cartoon face image.

Method and apparatus for generating cartoon face image, and computer storage medium

An image processing method includes: obtaining audio data corresponding to a reality scene image acquired in real time; dynamically determining attribute information of a virtual object according to the audio data, the attribute information indicating a visual state of the virtual object; identifying a target object from the reality scene image; determining, according to the target object, a fusion location of the virtual object determined according to the attribute information in the reality scene image according to the target object; fusing the virtual object determined according to the attribute information into the reality scene image according to the fusion location, the virtual object presenting different visual states that correspond to different attribute information dynamically determined according to the audio data.

Image processing method, storage medium, and computer device

The present disclosure provides an image processing method, including: recognizing a source object in a source image, and determining, according to feature points of the source object, an orientation and a size of the source object; adjusting, according to matching relationships between the orientation and the size of the source object and the orientation and the size of the target object, the orientation and the size of the source object; adjusting a shape of the source object and a shape of the target object according to an average shape of the source object and an average shape of the target object; and fusing, in real time, the source image and the target image in a manner of aligning the shape of the source object with the shape of the target object.

Image processing method, apparatus, terminal, and storage medium

The present disclosure discloses an image processing method, including: recognizing basic feature points of a source object in a source image and basic feature points of a base object in a base image; determining, according to distribution of the basic feature points in each of the images, auxiliary feature points that meet a filling condition according to the distribution in a corresponding image; determining feature points of a same type in the source object and the base object, the feature points of the same type including basic feature points of a same type and auxiliary feature points of a same type in the source object and the base object; determining average feature points according to positions of the feature points of the same type; deforming the source object and the base object according to the average feature points; and fusing the deformed source object with the deformed base object. The present disclosure further discloses an image processing apparatus, a terminal, and a storage medium.

Image processing method, apparatus, terminal, and storage medium for fusing images of two objects

Embodiments of this application disclose an image fusion method performed by a computing device. The method includes the following steps: obtaining source face image data of a current to-be-fused image and resource configuration information of a current to-be-fused resource, performing image recognition processing on the source face image data, to obtain source face feature points corresponding to the source face image data, and generating a source face three-dimensional grid of the source face image data according to the source face feature points, performing grid fusion by using a resource face three-dimensional grid and the source face three-dimensional grid to generate a target face three-dimensional grid, and performing face complexion fusion by using source complexion data of the source face image data and resource complexion data of resource face image data on the target face three-dimensional grid, to generate fused target face image data.

Image fusion method and device, storage medium and terminal

Aspects of the disclosure provide methods and apparatuses for generating video data. In some examples, an apparatus for generating the video data includes processing circuitry. The processing circuitry obtains tempo information from audio data that is inserted in a target video. The processing circuitry determines a plurality of target tempo points in the tempo information according to video effect time description information included in a video effect description file. The video effect description file includes video effect data that is used to adjust one or more video frames of an original video. The processing circuitry obtains the one or more video frames from the original video according to the plurality of target tempo points. The processing circuitry adjusts the one or more video frames with the video effect data. The processing circuitry generates the target video including the adjusted one or more video frames and the audio data.

Video data generation method, computer device, and storage medium

A method for image processing, an apparatus having a processing circuitry and non-transitory computer readable medium are described. For example, the processing circuitry extracts, from a first image of a face, first values of feature points of a facial organ on the face. The feature points of the facial organ are indicative of characteristics of the facial organ. Then, the processing circuitry calculates first distances between the feature points of the facial organ based on the first values of the feature points. Further, the processing circuitry calculates feature parameters of the facial organ according to the first distances between the feature points. Then, the processing circuitry detects a facial action of the face when the feature parameters satisfy a preset condition associated with the facial action.

Facial image processing method and apparatus, and storage medium

This application discloses a multimedia file joining method performed by an apparatus. After obtaining a first video clip and a second video clip to be joined, the apparatus obtains an audio file corresponding to the first video clip and the second video clip. The audio file records the first start and end time points of the first video clip and the second start and end time points of the second video clip. The apparatus adjusts the first video clip to play the first video clip in a first time period indicated by the first and end time points, and adjusts the second video clip to play the second video clip in a second time period indicated by the second and end time points with the first time period not overlapping the second time period. Finally, the apparatus performs a joining operation on the adjusted first video clip and the adjusted second video clip, to obtain a joined video file.

Multimedia file joining method and apparatus

The present disclosure discloses a media information processing method performed by a media information processing apparatus. The apparatus determines media information clips of target media information and their characteristics, and generates a first media information clip of a first user based on the determined characteristics. Next the apparatus determines media information clips other than the target media information clip in the target media information, and obtains a second media information clip corresponding to the characteristics of the determined media information clips. The apparatus then determines a splicing manner of the media information clips in the target media information, and splices the first media information clip and the second media information clip based on the determined splicing manner. By implementing the present disclosure, a media information clip obtained by a user through photographing and media information obtained by the user through imitation can be efficiently and seamlessly integrated.

Splicing user generated clips into target media information

A digital photo processing method, information processing apparatus, and non-transitory computer-readable medium. New digital photos are arranged in chronological order based on capture times of the new digital photos. A determination is made as to whether a subset of the new digital photos are related to each other based on one or a combination of the capture times of the new digital photos and locations at which the new digital photos were captured. One of a plurality of processing templates is selected based on one or a combination of at least one of the capture times of the subset of the new digital photos and at least one of the locations at which the subset of the new digital photos were captured. The subset of the new digital photos is processed according to the selected one of the plurality of processing templates to generate a single image.

Picture processing method and apparatus

A picture processing method and apparatus are described. The method includes: detecting newly-added pictures in a photo album in a computing device; acquiring information about the newly-added pictures, the information including either or both of a photographing time and a photographing place; updating a to-be-processed picture set according to the information about the newly-added pictures; selecting a picture processing template matching the to-be-processed picture set; and processing pictures in the to-be-processed picture set by using the picture processing template. The method can automatically process a newly-added picture, thereby reducing time costs of a user for picture processing.

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Shenzhen, China

Bin Fu