We introduce Reangle-A-Video, a unified framework for generating synchronized multi-view videos from a single input video. Unlike mainstream approaches that train multi-view video diffusion models on large-scale 4D datasets, our method reframes the multi-view video generation task as video-to-videos translation, leveraging publicly available image and video diffusion priors. In essence, Reangle-A-Video operates in two stages. (1) Multi-View Motion Learning: An image-to-video diffusion transformer is synchronously fine-tuned in a self-supervised manner to distill view-invariant motion from a set of warped videos. (2) Multi-View Consistent Image-to-Images Translation: The first frame of the input video is warped and inpainted into various camera perspectives under an inference-time cross-view consistency guidance using DUSt3R, generating multi-view consistent starting images. Extensive experiments on static view transport and dynamic camera control show that Reangle-A-Video surpasses existing methods, establishing a new solution for multi-view video generation. We will publicly release our code and data. Project page: https://hyeonho99.github.io/reangle-a-video/

本研究解决了如何从单一输入视频生成同步的多视角视频这一关键问题。通过将多视角视频生成任务重新构建为视频-视频转换，结合公开可用的图像和视频扩散先验，Reangle-A-Video提出了一种创新的方法。实验结果表明，该方法在静态视图传输和动态相机控制方面的表现优于现有技术，推动了多视角视频生成的新进展。

Reangle-A-Video：将4D视频生成视为视频到视频的转换