Direct-a-Video: Customized Video Generation with User-Directed Camera
  Movement and Object Motion
Shiyuan Yang Liang Hou Haibin Huang Chongyang Ma Pengfei Wan Di Zhang Xiaodong Chen Jing Liao
Recent text-to-video diffusion models have achieved impressive progress. In
practice, users often desire the ability to control object motion and camera
movement independently for customized video creation. However, current methods
lack the focus on separately controlling object motion and camera movement in a
decoupled manner, which limits the controllability and flexibility of
text-to-video models. In this paper, we introduce Direct-a-Video, a system that
allows users to independently specify motions for one or multiple objects
and/or camera movements, as if directing a video. We propose a simple yet
effective strategy for the decoupled control of object motion and camera
movement. Object motion is controlled through spatial cross-attention
modulation using the model's inherent priors, requiring no additional
optimization. For camera movement, we introduce new temporal cross-attention
layers to interpret quantitative camera movement parameters. We further employ
an augmentation-based approach to train these layers in a self-supervised
manner on a small-scale dataset, eliminating the need for explicit motion
annotation. Both components operate independently, allowing individual or
combined control, and can generalize to open-domain scenarios. Extensive
experiments demonstrate the superiority and effectiveness of our method.
Project page: https://direct-a-video.github.io/.