Control-A-Video: Controllable Text-to-Video Generation with Diffusion
  Models
Authors: name: Weifeng Chen, name: Jie Wu, name: Pan Xie, name: Hefeng Wu, name: Jiashi Li, name: Xin Xia, name: Xuefeng Xiao, name: Liang Lin

This paper presents a controllable text-to-video (T2V) diffusion model, named
Video-ControlNet, that generates videos conditioned on a sequence of control
signals, such as edge or depth maps. Video-ControlNet is built on a pre-trained
conditional text-to-image (T2I) diffusion model by incorporating a
spatial-temporal self-attention mechanism and trainable temporal layers for
efficient cross-frame modeling. A first-frame conditioning strategy is proposed
to facilitate the model to generate videos transferred from the image domain as
well as arbitrary-length videos in an auto-regressive manner. Moreover,
Video-ControlNet employs a novel residual-based noise initialization strategy
to introduce motion prior from an input video, producing more coherent videos.
With the proposed architecture and strategies, Video-ControlNet can achieve
resource-efficient convergence and generate superior quality and consistent
videos with fine-grained control. Extensive experiments demonstrate its success
in various video generative tasks such as video editing and video style
transfer, outperforming previous methods in terms of consistency and quality.
Project Page: https://controlavideo.github.io/