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
- Project Page: jiah-cloud.github.io/Track4World
- Repository: GitHub Repository
πΌοΈ 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.
βοΈ 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.
π 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}
}