Buckets:
| license: other | |
| pipeline_tag: other | |
| tags: | |
| - 3d-tracking | |
| - video-understanding | |
| - 4d-reconstruction | |
| - computer-vision | |
| # Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels | |
| Track4World is a feedforward model for efficient holistic 3D tracking of every pixel in a world-centric coordinate system from a monocular video. Built on a global 3D scene representation, Track4World applies a novel 3D correlation scheme to simultaneously estimate the pixel-wise 2D and 3D dense flow between arbitrary frame pairs. | |
| * **Paper:** [Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels](https://huggingface.co/papers/2603.02573) | |
| * **Project Page:** [jiah-cloud.github.io/Track4World](https://jiah-cloud.github.io/Track4World.github.io/) | |
| * **Repository:** [GitHub Repository](https://github.com/TencentARC/Track4World) | |
| --- | |
| ### 🖼️ Framework | |
| Track4World estimates dense 3D scene flow of every pixel between arbitrary frame pairs from a monocular video in a global feedforward manner, enabling efficient and dense 3D tracking of every pixel in the world-centric coordinate system. | |
| --- | |
| ## ⚙️ Setup and Installation | |
| ```bash | |
| # Clone the repository with submodules | |
| git clone --recursive https://github.com/TencentARC/Track4World.git | |
| cd Track4World | |
| # Create and activate environment | |
| conda create -n track4world python=3.11 | |
| conda activate track4world | |
| # Install PyTorch | |
| pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121 | |
| # Install dependencies | |
| pip install -r requirements.txt | |
| ``` | |
| Please refer to the [official GitHub README](https://github.com/TencentARC/Track4World) for detailed instructions on installing third-party modules and downloading weights. | |
| --- | |
| ## 🚀 Sample Usage | |
| You can perform tracking and reconstruction on the provided demo video using the following commands: | |
| ### First Frame 3D Tracking (`3d_ff`) | |
| ```bash | |
| python demo.py \ | |
| --mp4_path demo_data/cat.mp4 \ | |
| --mode 3d_ff \ | |
| --Ts -1 \ | |
| --save_base_dir results/cat | |
| ``` | |
| ### Dense Tracking: Every Pixel, Every Frame (`3d_efep`) | |
| ```bash | |
| python demo.py \ | |
| --mp4_path demo_data/cat.mp4 \ | |
| --coordinate world_depthanythingv3 \ | |
| --mode 3d_efep \ | |
| --Ts -1 \ | |
| --ckpt_init checkpoints/track4world_da3.pth \ | |
| --save_base_dir results/cat | |
| ``` | |
| --- | |
| ## Citation | |
| If you find Track4World useful for your research, please cite: | |
| ```bibtex | |
| @article{lu2026track4world, | |
| title = {Track4World: Feedforward World-Centric Dense 3D Tracking of All Pixels}, | |
| author = {Jiahao Lu and Jiayi Xu and Wenbo Hu and Ruijie Zhu and Chengfeng Zhao and Sai-Kit Yeung and Ying Shan and Yuan Liu}, | |
| journal = {arXiv preprint arXiv:2603.02573}, | |
| year = {2026} | |
| } | |
| ``` |
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