title: Vivid4D: Improving 4D Reconstruction from Monocular Video by Video
    Inpainting
summary: Reconstructing 4D dynamic scenes from casually captured monocular videos is
  valuable but highly challenging, as each timestamp is observed from a single
  viewpoint. We introduce Vivid4D, a novel approach that enhances 4D monocular
  video synthesis by augmenting observation views - synthesizing multi-view
  videos from a monocular input. Unlike existing methods that either solely
  leverage geometric priors for supervision or use generative priors while
  overlooking geometry, we integrate both. This reformulates view augmentation as
  a video inpainting task, where observed views are warped into new viewpoints
  based on monocular depth priors. To achieve this, we train a video inpainting
  model on unposed web videos with synthetically generated masks that mimic
  warping occlusions, ensuring spatially and temporally consistent completion of
  missing regions. To further mitigate inaccuracies in monocular depth priors, we
  introduce an iterative view augmentation strategy and a robust reconstruction
  loss. Experiments demonstrate that our method effectively improves monocular 4D
  scene reconstruction and completion.
paper_authors._id: 6800a43e1fd95d7dc21d6b83
paper_authors.name: Jiaxin Huang
paper_authors.status: claimed_verified
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paper_authors.fullname: Jiaxin Huang
paper_authors.type: user
paper_authors.user: JaceyH919
paper_authors._id: 6800a43e1fd95d7dc21d6b84
paper_authors.name: Sheng Miao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-17T11:13:53.013Z
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paper_authors.fullname: miao
paper_authors.type: user
paper_authors.user: shengmiao
paper_authors._id: 6800a43e1fd95d7dc21d6b85
paper_authors.name: BangBnag Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T10:13:59.176Z
paper_authors._id: 63d748ff6f49aa82306b7e48
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paper_authors.fullname: BB Yang
paper_authors.type: user
paper_authors.user: ybbbbt
paper_authors._id: 6800a43e1fd95d7dc21d6b86
paper_authors.name: Yuewen Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-17T11:14:07.429Z
paper_authors._id: 641a74bdf5e9c66105015385
paper_authors.avatarUrl: /avatars/13f966357de0ef5bb192ae566ad85ca0.svg
paper_authors.fullname: Zack Ma
paper_authors.type: user
paper_authors.user: yuewenma
paper_authors._id: 6800a43e1fd95d7dc21d6b87
paper_authors.name: Yiyi Liao
paper_title: Vivid4D: Improving 4D Reconstruction from Monocular Video by Video
    Inpainting
paper_summary: Reconstructing 4D dynamic scenes from casually captured monocular videos is
  valuable but highly challenging, as each timestamp is observed from a single
  viewpoint. We introduce Vivid4D, a novel approach that enhances 4D monocular
  video synthesis by augmenting observation views - synthesizing multi-view
  videos from a monocular input. Unlike existing methods that either solely
  leverage geometric priors for supervision or use generative priors while
  overlooking geometry, we integrate both. This reformulates view augmentation as
  a video inpainting task, where observed views are warped into new viewpoints
  based on monocular depth priors. To achieve this, we train a video inpainting
  model on unposed web videos with synthetically generated masks that mimic
  warping occlusions, ensuring spatially and temporally consistent completion of
  missing regions. To further mitigate inaccuracies in monocular depth priors, we
  introduce an iterative view augmentation strategy and a robust reconstruction
  loss. Experiments demonstrate that our method effectively improves monocular 4D
  scene reconstruction and completion.
paper_ai_summary: A method integrates geometric and generative priors for synthesizing multi-view videos from a single viewpoint, enhancing 4D scene reconstruction.
paper_ai_keywords: monocular videos 4D dynamic scenes Vivid4D video inpainting monocular depth priors iterative view augmentation robust reconstruction loss