At this blooming age of social media and mobile platform, mass consumers are migrating from horizontal video to vertical contents delivered on hand-held devices. Accordingly, revitalizing the exposure of horizontal video becomes vital and urgent, which is hereby tackled by our automated horizontal-to-vertical (abbreviated as H2V) video conversion framework. Essentially, the {\it \textbf{H2V}} framework performs subject-preserving video cropping instantiated in the proposed Rank-SS module. Rank-SS incorporates object detection to discover candidate subjects, from which we select the primary subject-to-preserve leveraging location, appearance, and salient cues in a convolutional neural network. In addition to converting horizontal videos vertically by cropping around the selected subject, automatic shot detection and multi-object tracking are integrated into the {\it \textbf{H2V}} framework to accommodate long and complex videos. To develop {\it \textbf{H2V}} systems, we collect an {\it \textbf{H2V-142K}} dataset containing 125 videos (132K frames) and 9,500 cover images annotated with primary subject bounding boxes. On {\it \textbf{H2V-142K}} and public object detection datasets, our method demonstrates promising results on the subject selection comparing to the related solutions. Furthermore, our {\it \textbf{H2V}} framework is industrially deployed hosting millions of daily active users and exhibits favorable H2V conversion performance. By making this dataset as well as our approach publicly available, we wish to pave the way for more horizontal-to-vertical video conversion research. Our collected H2V-142K dataset is available atH2V-142K website.

A style-based architecture (StyleGAN2) yields outstanding results in data-driven unconditional generative image modeling. This work proposes a Domain-guided Noise-optimization-based Inversion (DNI) method to perform facial image manipulation. It works based on an inverse code that includes: 1) a novel domain-guided encoder called Image2latent to project the image to StyleGAN2 latent space, which can reconstruct an input image with high-quality and maintain its semantic meaning well; 2) a noise optimization mechanism in which a set of noise vectors are used to capture the high-frequency details such as image edges, further improving image reconstruction quality; and 3) a mask for seamless image fusion and local style migration. We further propose a novel semantic alignment evaluation pipeline. It evaluates the semantic alignment with an inverse code by using different attribute boundaries. Extensive qualitative and quantitative comparisons show that DNI can capture rich semantic information and achieve a satisfactory image reconstruction. It can realize a variety of facial image manipulation tasks and outperform state of the art.

One problem in subdivision surfaces is the number of facets grows exponentially with the level of subdivision. Subdivision schemes are cost intensive at higher levels of subdivision. In this paper, we propose an adaptive subdivision scheme for subdivision surfaces based on triangular meshes and exploit the local smoothness information of a surface for adaptive refinement of a model. With this approach, we can avoid unnecessary subdivision in relative smooth areas and represent surfaces with lower cost when compared with those obtained by uniform subdivision schemes. We compare our methods for various 3D graphic meshes and present our results.