Video harmonization aims to adjust the foreground of a composite video to make it compatible with the background. So far, video harmonization has only received limited attention and there is no public dataset for video harmonization. In this work, we construct a new video harmonization dataset HYouTube by adjusting the foreground of real videos to create synthetic composite videos. Moreover, we consider the temporal consistency in video harmonization task. Unlike previous works which establish the spatial correspondence, we design a novel framework based on the assumption of color mapping consistency, which leverages the color mapping of neighboring frames to refine the current frame. Extensive experiments on our HYouTube dataset prove the effectiveness of our proposed framework. Our dataset and code are available at https://github.com/bcmi/Video-Harmonization-Dataset-HYouTube.

Static image action recognition is a challenging task due to the lack of motion information in a static image. Some previous works have attempted to hallucinate the motion information in a static image using a generator learnt from freely available unlabeled videos. However, their hallucinated motion information is either low-level or coarse-grained, which may contain lots of noise or lose motion details. In contrast, we propose to hallucinate fine-grained high-level motion information, which is more robust and detail-preserving. Specifically, we hallucinate motion feature map which encodes the motion details of human body parts. We also hallucinate motion attention map to focus on motion-related regions. Our hallucinated motion information can greatly facilitate static image action recognition, which is confirmed by the experiments on two static action image datasets and two video datasets.