TrackGo: A Flexible and Efficient Method for Controllable Video Generation

Recent years have seen substantial progress in diffusion-based controllable video generation. However, achieving precise control in complex scenarios, including fine-grained object parts, sophisticated motion trajectories, and coherent background movement, remains a challenge. In this paper, we introduce *TrackGo*, a novel approach that leverages free-form masks and arrows for conditional video generation. This method offers users with a flexible and precise mechanism for manipulating video content. We also propose the *TrackAdapter* for control implementation, an efficient and lightweight adapter designed to be seamlessly integrated into the temporal self-attention layers of a pretrained video generation model. This design leverages our observation that the attention map of these layers can accurately activate regions corresponding to motion in videos. Our experimental results demonstrate that our new approach, enhanced by the TrackAdapter, achieves state-of-the-art performance on key metrics such as FVD, FID, and ObjMC scores.

Authors

• Haitao Zhou Beihang University, AIsphere Inc.
• Chuang Wang Beihang University, AIsphere Inc.
• Rui Nie Beihang University
• Jinlin Liu AIsphere Inc.
• Dongdong Yu AIsphere Inc.
• Qian Yu Beihang University
• Changhu Wang University of California, Los Angeles

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