TrackGo: A Flexible and Efficient Method for Controllable Video
  Generation
Authors: name: Haitao Zhou, name: Chuang Wang, name: Rui Nie, name: Jinxiao Lin, name: Dongdong Yu, name: Qian Yu, name: Changhu Wang

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. The project page of TrackGo
can be found at: https://zhtjtcz.github.io/TrackGo-Page/