World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos. Our approach conditions a video model on dense, pixel-aligned renderings that encode appearance, geometry, and 3D scene motion along both input and target camera trajectories to correct rendering artifacts and fill in missing regions from an initial reconstruction. To train this model, we construct a dataset of aligned multiview video pairs and dynamic 3DGS representations, with simulated artifacts characteristic of monocular reconstruction. At test time, we distil
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- LinkedLinked via arxiv author · 85%Liyuan Zhu →
“World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video”
- LinkedLinked via arxiv author · 85%Shengyu Huang →
“World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video”
- LinkedLinked via arxiv author · 85%Amrita Mazumdar →
“World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video”
- LinkedLinked via arxiv author · 85%Tianye Li →
“World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video”
- LinkedLinked via arxiv author · 85%Zan Gojcic →
“World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video”
- LinkedLinked via arxiv author · 85%Gordon Wetzstein →
“World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video”
- LinkedLinked via arxiv author · 85%Iro Armeni →
“World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video”
- LinkedLinked via arxiv author · 85%Shalini De Mello →
“World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video”