arXiv cs.AI· Liyuan Zhu, Shengyu Huang, Amrita Mazumdar, Tianye Li, Zan Gojcic, Gordon Wetzstein, Iro Armeni, Shalini De Mello, Alex Trevithick·· 3 小时前AI 评分41
World from Motion：从单目视频生成动态 3D Gaussian 重建
World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
AI 导读
NeurIPS 2026 论文提出 World from Motion，一种从单目视频生成可自由渲染的动态 3D Gaussian 表示的方法。该方法用像素对齐的密集渲染条件化视频模型，修复初始重建的渲染伪影并补全缺失区域，测试时将生成内容蒸馏回单一一致的动态 3DGS。它在 4D 重建上创下新 SOTA，并能泛化到有大视角变化和动态运动的真实场景视频。
正文 · 原文
Abstract: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 distill the model's generations, including newly observed regions and motions, back into a single consistent, high-quality dynamic 3DGS, improving both novel-view synthesis and the underlying 3D motion. Our method sets a new state of the art in 4D reconstruction and seamlessly generalizes to in-the-wild videos with large viewpoint changes and dynamic motions.
| Comments: | NeurIPS 2026 Camera Ready. Project page: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR) |
| Cite as: | arXiv:2607.01202 [cs.CV] |
| (or arXiv:2607.01202v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01202 arXiv-issued DOI via DataCite |
Submission history
From: Liyuan Zhu [view email]
[v1]
Wed, 1 Jul 2026 17:41:00 UTC (6,531 KB)
[v2]
Fri, 23 Oct 2026 04:36:58 UTC (13,196 KB)
来源：arXiv cs.AI · arxiv.org