title: Reangle-A-Video: 4D Video Generation as Video-to-Video Translation
summary: 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/
paper_authors._id: 67d2784fbe3b4e06086d8eec
paper_authors.name: Hyeonho Jeong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-13T08:23:54.861Z
paper_authors._id: 656ee8008bb9f4f8d95bd8f7
paper_authors.avatarUrl: /avatars/4069d70f1279d928da521211c495d638.svg
paper_authors.fullname: Hyeonho Jeong
paper_authors.type: user
paper_authors.user: hyeonho-jeong-video
paper_authors._id: 67d2784fbe3b4e06086d8eed
paper_authors.name: Suhyeon Lee
paper_authors._id: 67d2784fbe3b4e06086d8eee
paper_authors.name: Jong Chul Ye
paper_title: Reangle-A-Video: 4D Video Generation as Video-to-Video Translation
paper_summary: 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/
paper_ai_keywords: image-to-video diffusion transformer self-supervised manner view-invariant motion warped videos Multi-View Consistent Image-to-Images Translation cross-view consistency DUSt3R multi-view video generation static view transport dynamic camera control