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: this https URL

本文提出了一种可控的文本到视频模型，名为 Video-ControlNet，它可以生成由控制信号（如边缘或深度图）条件的视频。该模型采用了一种新的残差噪声初始化策略来实现输入视频的运动先验，并生成具有细粒度控制的高质量和连贯视频。