Microsoft Technology Licensing, LLC

Jing Liao

Lu Yuan

Dongdong Chen

Gang Hua

Hugues Herve Hoppe

Mark Thomas Finch, Iii

Kaidi Cao

Mingming He

Mark Thomas Iii Finch

Image stylization is based on a learning network. A learning network is trained with a plurality of images and a reference image with a particular texture style. A plurality of different sub-networks of the learning network is trained, respectively. Specifically, one of the sub-networks is trained to extract one or more feature maps from the source image and transform the feature maps with the texture style applied thereon to a target image. Each of the feature maps indicates part of feature information of the source image. Another sub-network is trained to apply a specified texture style to the extracted feature maps, such that the target image generated based on the processed feature maps can embody the specified texture style.

Image stylization based on learning network

According to implementations of the subject matter described herein, there is provided an image colorization solution. The solution includes determining a similarity between contents presented in a grayscale source image and a color reference image and determining a col or target image corresponding to the source image based on the similarity. Specifically, a first and a second sets of blocks similar and dissimilar to the reference image are determined based on the similarity; a first color for the first set of blocks is determined based on a color of corresponding blocks in the reference image; a second color for the second set of blocks is determined independently of the reference image. Through this solution, it is possible to provide user controllability and customized effects in colorization, and there is no strict requirement on correspondence between the color image and grayscale image, achieving more robustness to selection of color reference images.

Image colorization based on reference information

Implementations of the present disclosure provide a solution for cross-domain image translation. In this solution, a first learning network for geometric deformation from a first to a second image domain is determined based on first and second images in the first and second domains, images in the two domains having different styles and objects in the images having geometric deformation with respect to each other. Geometric deformation from the second to the first domains is performed on the second image or geometric deformation from the first to the second domains is performed on the first image, to generate an intermediate image. A second learning network for style transfer from the first to the second domains is determined based on the first and intermediate images or based on the second and intermediate images generated. Accordingly, processing accuracy of leaning networks for cross-domain image translation can be improved and complexity is lowered.

Cross-domain image translation

In accordance with implementations of the subject matter described herein, there is proposed a solution of visual stylization of stereoscopic images. In the solution, a first feature map for a first source image and a second feature map for a second source image are extracted. The first and second source images correspond to first and second views of a stereoscopic image, respectively. A first unidirectional disparity from the first source image to the second source image is determined based on the first and second source images. First and second target images having a specified visual style are generated by processing the first and second feature maps based on the first unidirectional disparity. Through the solution, a disparity between two source images of a stereoscopic image are taken into account when performing the visual style transfer, thereby maintaining a stereoscopic effect in the stereoscopic image consisting of the target images.

Visual stylization on stereoscopic images

Various technologies described herein pertain to generating an output video loop from an input video that includes values at pixels over a time range. Respective input time intervals within the time range of the input video are determined for the pixels by performing an optimization. The optimization can be performed to assign the input time intervals at a first level of resolution, while terms of an objective function use a finer, second level of resolution. An input time interval for a particular pixel includes a per-pixel loop period of a single, contiguous loop at the particular pixel within the time range from the input video. The input time intervals can be temporally scaled based on per-pixel loop periods and an output video loop period. The output video loop is created based on the values at the pixels over the input time intervals for the pixels in the input video.

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Jing Liao