Reangle-A-Video: 4D Video Generation as Video-to-Video Translation
Authors: name: Hyeonho Jeong, name: Suhyeon Lee, name: Jong Chul Ye

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/