Vivid4D: Improving 4D Reconstruction from Monocular Video by Video
  Inpainting
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.
6800a43e1fd95d7dc21d6b83
Jiaxin Huang
claimed_verified
2025-04-17T08:03:59.476Z
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Jiaxin Huang
user
JaceyH919
6800a43e1fd95d7dc21d6b84
Sheng Miao
admin_assigned
2025-04-17T11:13:53.013Z
677c94b658cfcc3df8a9d80e
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miao
user
shengmiao
6800a43e1fd95d7dc21d6b85
BangBnag Yang
claimed_verified
2025-04-22T10:13:59.176Z
63d748ff6f49aa82306b7e48
/avatars/9f0b8b8a09b14d76e52ed1bd312e6b63.svg
BB Yang
user
ybbbbt
6800a43e1fd95d7dc21d6b86
Yuewen Ma
admin_assigned
2025-04-17T11:14:07.429Z
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/avatars/13f966357de0ef5bb192ae566ad85ca0.svg
Zack Ma
user
yuewenma
6800a43e1fd95d7dc21d6b87
Yiyi Liao
Vivid4D: Improving 4D Reconstruction from Monocular Video by Video
  Inpainting
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.
A method integrates geometric and generative priors for synthesizing multi-view videos from a single viewpoint, enhancing 4D scene reconstruction.
monocular videos 4D dynamic scenes Vivid4D video inpainting monocular depth priors iterative view augmentation robust reconstruction loss