Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning

CVPR, 2024
Xinshun Wang* · Zhongbin Fang*
Xia Li · Xiangtai Li · Chen Chen · Mengyuan Liu✉

This is the official PyTorch implementation of the paper "Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning" (CVPR 2024).

🙂News

• [Apr 23, 2024] Code is released.
• [Feb 27, 2024] Paper is accepted by CVPR 2024!
• [Dec 07, 2023] Paper is released and GitHub repo is created.

😃Run

1. Installation

conda create -n skeleton_in_context python=3.7 anaconda
conda activate skeleton_in_context
pip install -r requirements.txt

2. Data Preparation

There are 2 ways to prepare data:

1) Download ready-to-use data

You can download ready-to-use data here, and unzip the files in data/. After you do so, the data/ directory should look like this:

data/
│
├── 3DPW_MC/
│   ├── train/
│   └── test/
│
├── AMASS/
│   ├── train/
│   └── test/
│
├── H36M/
|   ├── train/
|   └── test/
│
├── H36M_FPE/
|   ├── train/
|   └── test/
|
├── source_data/
|   └── H36M.pkl
|
└── support_data/

Now you are ready to train and evaluate Skeleton-in-Context.

2) Download source data and pre-process them

Human3.6M:

Download MotionBERT's Human3.6M data here, unzip to data/source_data/, and rename it H36M.pkl. Please refer to MotionBERT for how the Human3.6M data are processed.

AMASS:

Download AMASS data here. The AMASS data directory should look like this:

data/source_data/AMASS/
├── ACCAD/
├── BioMotionLab_NTroje/
├── CMU/
├── EKUT/
├── Eyes_Japan_Dataset/
├── KIT/
├── MPI_Limits/
├── TCD_handMocap/
└── TotalCapture/

3DPW:

Download 3DPW data here. The 3DPW data directory should look like this:

data/source_data/PW3D/
└── sequenceFiles/
    ├── test/
    ├── train/
    └── validation/

Pre-process:

Pre-process the data by running the following lines:

python data_gen/convert_h36m_PE.py
python data_gen/convert_h36m_FPE.py
python data_gen/convert_amass_MP.py
python data_gen/convert_3dpw_MC.py
python data_gen/calculate_avg_pose.py

Now you are ready to train and evaluate Skeleton-in-Context.

3. Training

To train Skeleton-in-Context, run the following command:

CUDA_VISIBLE_DEVICES=<GPU> python train.py --config configs/default.yaml --checkpoint ckpt/[YOUR_EXP_NAME]

4. Evaluation

To evaluate Skeleton-in-Context, run the following command:

CUDA_VISIBLE_DEVICES=<GPU> python train.py --config configs/default.yaml --evaluate ckpt/[YOUR_EXP_NAME]/[YOUR_CKPT]

For example:

CUDA_VISIBLE_DEVICES=<GPU> python train.py --config configs/default.yaml --evaluate ckpt/pretrained/latest_epoch.bin

License

MIT License

Citation

If you find our work useful in your research, please consider citing:

@article{wang2023skeleton,
  title={Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning},
  author={Wang, Xinshun and Fang, Zhongbin and Li, Xia and Li, Xiangtai and Chen, Chen and Liu, Mengyuan},
  journal={arXiv preprint arXiv:2312.03703},
  year={2023}
}

Acknowledgement

This work is inspired by Point-In-Context. The code for our work is built upon MotionBERT. Our tribute to these excellent works, and special thanks to the following works: siMLPe, EqMotion, STCFormer, GLA-GCN.