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.


πŸ–ΌοΈ 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.


βš™οΈ Setup and Installation

# 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 for detailed instructions on installing third-party modules and downloading weights.


πŸš€ Sample Usage

You can perform tracking and reconstruction on the provided demo video using the following commands:

First Frame 3D Tracking (3d_ff)

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)

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:

@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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