GradientSpaces / World-from-Motion
git clone https://github.com/GradientSpaces/World-from-Motion.git
[NeurIPS 2026] World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Videos
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LanguagePython
LicenseApache-2.0
Created2026-10-15 (7d ago)
Last push3d ago
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- GitHub: github.com/GradientSpaces/World-from-Motion
- Homepage: https://research.nvidia.com/labs/amri/projects/world-from-motion/
- Releases · Issues · Hacker News mentions
README excerpt
# World from Motion ### Generative Dynamic Gaussian Reconstruction from Monocular Video **NeurIPS 2026** Liyuan Zhu 1,2 · Shengyu Huang 2 · Amrita Mazumdar 2 · Tianye Li 2 · Zan Gojcic 2 Gordon Wetzstein 1 · Iro Armeni 1 · Shalini De Mello 2 · Alex Trevithick 2 1 Stanford University 2 NVIDIA **Project page** · **Paper** · **Teaser** · **Interactive viewer** World from Motion turns a monocular video into a dynamic 3D Gaussian scene that can be rendered from new viewpoints. A video model refines views rendered from an initial reconstruction, then those generated views are distilled back into the scene. The core video pipeline combines MoSca reconstruction with DiffSynth video generation. ## Getting started 1. Build the environment using the installation guide. 2. Prepare the model bundle. 3. Reconstruct your video with the usage guide. 4. Reproduce **Ours (sample)** and **Ours (distill)** on DyCheck using REPRODUCTION.md, including the benchmark-specific setup, commands and reference numbers. Model weights are distributed separately. The DyCheck Table 1 checkpoint is on Hugging Face at `liyzzz/World-from-Motion`, and the released evaluation masks are at `liyzzz/World-from-Motion-DyCheck-Masks`. The download location for the video-pipeline model bundle will be added when it is published. ## Code structure | Directory | Contents | | --- | --- | | `src/wfm/` | Video generation, camera controls and pipeline utilities | | `docs/` | Installation, model setup and video usage | | `scripts/` | Source-package checks | | `config/` | Model and inference settings | | `tests/` | Unit tests using synthetic inputs | | `reproduce/` | DyCheck sampling, distillation, evaluation and pinned research backends | | `vendor/` | Dependencies and their original licenses | ## Citation [code] ## Acknowledgements and license The original World from Motion code and the WfM step-12500 checkpoint are licensed under Apache-2.0. See NOTICE for copyright and model attribution, and model setup for checkpoint packaging. This implementation builds on MoSca, DiffSynth-Studio and the other components listed in THIRD_PARTY_NOTICES.md. Bundled third-party code and separately obtained models retain their respective licenses.