Reangle-A-Video: 4D Video Generation as Video-to-Video Translation
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/
67d2784fbe3b4e06086d8eec
Hyeonho Jeong
claimed_verified
2025-03-13T08:23:54.861Z
656ee8008bb9f4f8d95bd8f7
/avatars/4069d70f1279d928da521211c495d638.svg
Hyeonho Jeong
user
hyeonho-jeong-video
67d2784fbe3b4e06086d8eed
Suhyeon Lee
67d2784fbe3b4e06086d8eee
Jong Chul Ye
Reangle-A-Video: 4D Video Generation as Video-to-Video Translation
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/
Reangle-A-Video generates synchronized multi-view videos from a single input video using image-to-video diffusion transformers and cross-view consistency guidance.
video diffusion models 4D datasets video-to-videos translation image diffusion priors image-to-video diffusion transformer self-supervised learning view-invariant motion DUSt3R cross-view consistency static view transport dynamic camera control multi-view video generation