title: SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video
    Restoration
summary: Video restoration poses non-trivial challenges in maintaining fidelity while
  recovering temporally consistent details from unknown degradations in the wild.
  Despite recent advances in diffusion-based restoration, these methods often
  face limitations in generation capability and sampling efficiency. In this
  work, we present SeedVR, a diffusion transformer designed to handle real-world
  video restoration with arbitrary length and resolution. The core design of
  SeedVR lies in the shifted window attention that facilitates effective
  restoration on long video sequences. SeedVR further supports variable-sized
  windows near the boundary of both spatial and temporal dimensions, overcoming
  the resolution constraints of traditional window attention. Equipped with
  contemporary practices, including causal video autoencoder, mixed image and
  video training, and progressive training, SeedVR achieves highly-competitive
  performance on both synthetic and real-world benchmarks, as well as
  AI-generated videos. Extensive experiments demonstrate SeedVR's superiority
  over existing methods for generic video restoration.
paper_authors._id: 67779a845d0e6d1912b339a7
paper_authors.name: Jianyi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-05T23:02:39.129Z
paper_authors._id: 63043db17373aacccd89f49d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63043db17373aacccd89f49d/jzP_fPCFXeYJvAD8uA_N7.jpeg
paper_authors.fullname: JIANYI WANG
paper_authors.type: user
paper_authors.user: Iceclear
paper_authors._id: 67779a845d0e6d1912b339a8
paper_authors.name: Zhijie Lin
paper_authors._id: 67779a845d0e6d1912b339a9
paper_authors.name: Meng Wei
paper_authors._id: 67779a845d0e6d1912b339aa
paper_authors.name: Yang Zhao
paper_authors._id: 67779a845d0e6d1912b339ab
paper_authors.name: Ceyuan Yang
paper_authors._id: 67779a845d0e6d1912b339ac
paper_authors.name: Chen Change Loy
paper_authors._id: 67779a845d0e6d1912b339ad
paper_authors.name: Lu Jiang
paper_title: SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video
    Restoration
paper_summary: Video restoration poses non-trivial challenges in maintaining fidelity while
  recovering temporally consistent details from unknown degradations in the wild.
  Despite recent advances in diffusion-based restoration, these methods often
  face limitations in generation capability and sampling efficiency. In this
  work, we present SeedVR, a diffusion transformer designed to handle real-world
  video restoration with arbitrary length and resolution. The core design of
  SeedVR lies in the shifted window attention that facilitates effective
  restoration on long video sequences. SeedVR further supports variable-sized
  windows near the boundary of both spatial and temporal dimensions, overcoming
  the resolution constraints of traditional window attention. Equipped with
  contemporary practices, including causal video autoencoder, mixed image and
  video training, and progressive training, SeedVR achieves highly-competitive
  performance on both synthetic and real-world benchmarks, as well as
  AI-generated videos. Extensive experiments demonstrate SeedVR's superiority
  over existing methods for generic video restoration.
paper_ai_keywords: diffusion-based restoration diffusion transformer shifted window attention causal video autoencoder mixed image and video training progressive training synthetic and real-world benchmarks AI-generated videos generic video restoration