World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
- Published
- Source
- arXiv
- Paper number
- 547
- Field
- Computer Vision
- arXiv ID
- 2607.01202
Key points
- It is the first method to condition a video generation model on a dynamic 3DGS representation, rendering geometry, appearance, and 3D motion according to reference and target views.
- It achieves state-of-the-art results on the DyCheck benchmark with mPSNR 19.98, mSSIM 0.716, and mLPIPS 0.178.
- In the optimization step that distills generated samples back into dynamic 3DGS, it also improves 3D tracking accuracy, with PCK@0.05 rising from 0.824 for MoSca to 0.862.
- Reconstruction quality improves monotonically as the number of virtual cameras increases and converges after 8 cameras, with mPSNR rising from 18.69 to 19.78.
- It outperforms both generation-based methods such as CAT4D and Vista4D and reconstruction-based methods such as WorldTree and ViDAR.
- It can perform visual outpainting on in-the-wild video, dynamic correction, and out-of-view dynamics inference.
Paper links
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