Beijing Baidu Netcom Science and Technology Co., Ltd.

Tianwei Lin

Fu Li

Xin Li

Hao Sun

Henan Zhang

Errui Ding

Shilei Wen

Dongliang He

Xiang Zhao

Hongwu Zhang

Xiang Long

A meme generation method, an electronic device, and a storage medium are provided. The method includes: determining a plurality of second expression images corresponding to a target face image based on a plurality of first expression images contained in a first meme; generating a second meme corresponding to the target face image based on the plurality of second expression images corresponding to the target face image; wherein, determining an affine transformation parameter between the target face image and an i-th first expression image in the plurality of first expression images according to a corresponding relation between a face key point in the target face image and a face key point in the i-th first expression image; and transforming the target face image based on the affine transformation parameter to obtain an i-th second expression image corresponding to the target face image.

Meme generation method, electronic device and storage medium

A method and apparatus for processing a video frame are provided. The method may include: converting, using an optical flow generated based on a previous frame and a next frame of adjacent frames in a video, a feature map of the previous frame to obtain a converted feature map; determining, based on an error of the optical flow, a weight of the converted feature map, and obtaining a fused feature map based on a weighted result of a feature of the converted feature map and a feature of a feature map of the next frame; and updating the feature map of the next frame as the fused feature map.

Method and apparatus for processing video frame

The disclosure provides an image processing method, an image processing apparatus, an electronic device and a storage medium, which belongs to the field of computer technologies, and specifically relates to computing vision, image processing, face recognition, and deep learning technologies in artificial intelligence. The method includes: performing skin color recognition on a face image to be processed to determine a target skin color of a face contained in the face image; obtaining a reference transformation image corresponding to the face image by processing the face image using any style transfer model in response that a style transfer model set does not comprise a style transfer model corresponding to the target skin color; and obtaining a target transformation image matching the target skin color by adjusting a hue value, a saturation value, and a lightness value of each pixel in the target region based on the target skin color.

Image processing method, electronic device and storage medium for performing skin color recognition on a face image

A method includes screening, by a video-clip screening module in a video description model, a plurality of video proposal clips acquired from a video to be analyzed, to acquire a plurality of video clips suitable for description. The plural video proposal clips acquired from the video to be analyzed may be screened by the video-clip screening module to acquire the plural video clips suitable for description; and then, each video clip is described by a video-clip describing module, thus avoiding description of all the video proposal clips, only describing the screened video clips which have strong correlation with the video and are suitable for description, removing the interference of the description of the video clips which are not suitable for description in the description of the video, guaranteeing the accuracy of the final descriptions of the video clips, and improving the quality of the descriptions of the video clips.

Method and apparatus for detecting temporal action of video, electronic device and storage medium

A method and apparatus for detecting a temporal action of a video, an electronic device and a storage medium are disclosed, which relates to the field of video processing technologies. An implementation includes: acquiring an initial temporal feature sequence of a video to be detected; acquiring, by a pre-trained video-temporal-action detecting module, implicit features and explicit features of a plurality of configured temporal anchor boxes based on the initial temporal feature sequence; and acquiring, by the video-temporal-action detecting module, the starting position and the ending position of a video clip containing a specified action, the category of the specified action and the probability that the specified action belongs to the category from the plural temporal anchor boxes according to the explicit features and the implicit features of the plural temporal anchor boxes.

The disclosure discloses a cartoonlization processing method for an image, and relates to a field of computational vision, image processing, face recognition, deep learning technologies. The method includes: performing skin color recognition on a facial image to be processed to determine a target skin color of a face in the facial image; processing the facial image by utilizing any cartoonizing model in a cartoonizing model set to obtain a reference cartoonized image corresponding to the facial image in a case that the cartoonizing model set does not contain a cartoonizing model corresponding to the target skin color; determining a pixel adjustment parameter based on the target skin color and a reference skin color corresponding to the any cartoonizing model; and adjusting a pixel value of each pixel point in the reference cartoonized image based on the pixel adjustment parameter, to obtain a target cartoonized image corresponding to the facial image.

Cartoonlization processing method for image, electronic device, and storage medium

Embodiments of the present disclosure provide a method and apparatus for generating an image. The method may include: receiving a first image including a face input by a user in an interactive scene; presenting the first image to the user; inputting the first image into a pre-trained generative adversarial network in a backend to obtain a second image output by the generative adversarial network; where the generative adversarial network uses face attribute information generated based on the input image as a constraint; and presenting the second image to the user in response to obtaining the second image output by the generative adversarial network in the backend.

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Beijing, China

Tianwei Lin