🚃 TRAM

Official implementation for the paper:
TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos
Yufu Wang, Ziyun Wang, Lingjie Liu, Kostas Daniilidis
[Project Page]

Installation

1. Clone this repo with the --recursive flag.

git clone --recursive https://github.com/yufu-wang/tram

2. Creating a new anaconda environment.

conda create -n tram python=3.10 -y
conda activate tram
bash install.sh

3. Compile DROID-SLAM. If you encountered difficulty in this step, please refer to its official release for more info. In this project, DROID is modified to support masking.

cd thirdparty/DROID-SLAM
python setup.py install
cd ../..

Prepare data

Register at SMPLify and SMPL, whose usernames and passwords will be used by our script to download the SMPL models. In addition, we will fetch trained checkpoints and an example video. Note that thirdparty models have their own licenses.

Run the following to fetch all models and checkpoints to data/

bash scripts/download_models.sh

Run demo on videos

This project integrates the complete 4D human system, including tracking, slam, and 4D human capture in the world space. We separate the core functionalities into different scripts, which should be run sequentially. Each step will save its result to be used by the next step. All results will be saved in a folder with the same name as the video.

# 1. Run Masked Droid SLAM (also detect+track humans in this step)
python scripts/estimate_camera.py --video "./example_video.mov" 
# -- You can indicate if the camera is static. The algorithm will try to catch it as well.
python scripts/estimate_camera.py --video "./another_video.mov" --static_camera

# 2. Run 4D human capture with VIMO.
python scripts/estimate_humans.py --video "./example_video.mov"

# 3. Put everything together. Render the output video.
python scripts/visualize_tram.py --video "./example_video.mov"

Running the above three scripts on the provided video ./example_video.mov will create a folder ./results/exapmle_video and save all results in it. Please see available arguments in the scripts.

Training and evaluation

Code will come soon ...

Acknowledgements

We benefit greatly from the following open source works, from which we adapted parts of our code.

• WHAM: visualization and evaluation
• HMR2.0: baseline backbone
• DROID-SLAM: baseline SLAM
• ZoeDepth: metric depth prediction
• BEDLAM: large-scale video dataset
• EMDB: evaluation dataset

In addition, the pipeline includes Detectron2, Segment-Anything, and DEVA-Track-Anything.

Citation

@article{wang2024tram,
  title={TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos},
  author={Wang, Yufu and Wang, Ziyun and Liu, Lingjie and Daniilidis, Kostas},
  journal={arXiv preprint arXiv:2403.17346},
  year={2024}
}