Towards Robust and Expressive Whole-body Human Pose and Shape Estimation

Hui En Pang Zhongang Cai Lei Yang Qingyi Tao Zhonghua Wu Tianwei Zhang Ziwei Liu

S-Lab, Nanyang Technological University

NeurIPS 2023

[arXiv] • [Slides]

Getting started

Installation | Train | Evaluation

Introduction

This repo is official PyTorch implementation of Towards Robust and Expressive Whole-body Human Pose and Shape Estimation (NeurIPS2023).

Installation

# 1. Create a conda virtual environment.
conda create -n robosmplx python=3.8 -y
conda activate robosmplx

# 2. Install dependenciesv
conda install ffmpeg
conda install pytorch=1.8.0 torchvision cudatoolkit=10.2 -c pytorch
conda install -c fvcore -c iopath -c conda-forge fvcore iopath -y
conda install -c bottler nvidiacub -y
conda install pytorch3d -c pytorch3d

pip install mmcv_full-1.5.3-cp38-cp38-manylinux1_x86_64.whl
rm mmcv_full-1.7.1-cp38-cp38-manylinux1_x86_64.whl

# 3. Pull our code
git clone https://github.com/robosmplx/RoboSMPLX
cd RoboSMPLX
pip install -v -e .

Train (TBA)

Training with a single / multiple GPUs

python tools/train.py ${CONFIG_FILE} ${WORK_DIR} --no-validate

Example: using 1 GPU to train RoboSMPLX.

python tools/train.py ${CONFIG_FILE} ${WORK_DIR} --gpus 1 --no-validate

Training with Slurm

If you can run RoboSMPLX on a cluster managed with slurm, you can use the script slurm_train.sh.

./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} ${GPU_NUM} --no-validate

Common optional arguments include:

--resume-from ${CHECKPOINT_FILE}: Resume from a previous checkpoint file.
--no-validate: Whether not to evaluate the checkpoint during training.

Example: using 8 GPUs to train RoboSMPLX on a slurm cluster.

./tools/slurm_train.sh my_partition my_job configs/robosmplx/resnet50_hmr_pw3d.py work_dirs/hmr 8 --no-validate

You can check slurm_train.sh for full arguments and environment variables.

Training configs

sh tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} ${GPU_NUM}

# Stage 1a: train hand
sh tools/slurm_train.sh ${PARTITION} ${JOB_NAME} configs/robosmplx/robosmplx_hand.py expts/robosmplx_hand 8

# Stage 1b: train face
sh tools/slurm_train.sh ${PARTITION} ${JOB_NAME} configs/robosmplx/robosmplx_face.py expts/robosmplx_face 8

# Stage 1c: train body
sh tools/slurm_train.sh ${PARTITION} ${JOB_NAME} configs/robosmplx/robosmplx_body.py expts/robosmplx_body 8

# Stage 2: train wholebody
sh tools/slurm_train.sh ${PARTITION} ${JOB_NAME} configs/robosmplx/robosmplx.py expts/robosmplx_wholebody 8

# Stage 2b: FT wholebody on AGORA
sh tools/slurm_train.sh ${PARTITION} ${JOB_NAME} configs/robosmplx/robosmplx_agora.py expts/robosmplx_wholebody_agora 8

Evaluation (TBA)

For our robustness study, we evaluated on 10 different augmentations:

Vertical translation
Horizontal translation
Scale
Low Resolution
Rotation
Hue
Sharpness
Grayness
Contrast
Brightness

Evaluate with a single GPU / multiple GPUs

python tools/test.py ${CONFIG} --work-dir=${WORK_DIR} ${CHECKPOINT} --metrics=${METRICS} --augmentation=${AUGMENTATION}

Example:

python tools/test.py configs/robosmplx/robosmplx.py --work-dir=work_dirs/robosmplx work_dirs/robosmplx/latest.pth 

# Evaluation under augmentation
python tools/test.py configs/robosmplx/robosmplx.py --work-dir=work_dirs/robosmplx work_dirs/robosmplx/latest.pth --augmentation=rotation

Evaluate with slurm

If you can run MMHuman3D on a cluster managed with slurm, you can use the script slurm_test.sh.

./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} ${CONFIG} ${WORK_DIR} ${CHECKPOINT} --metrics ${METRICS}

Example:

./tools/slurm_test.sh my_partition test_robosmplx configs/robosmplx/robosmplx.py work_dirs/robosmplx work_dirs/robosmplx/latest.pth 1 

# Evaluation under augmentation
./tools/slurm_test.sh my_partition test_robosmplx configs/robosmplx/robosmplx.py work_dirs/robosmplx work_dirs/robosmplx/latest.pth 1 --augmentation rotation

Citation

If you find our work useful for your research, please consider citing the paper:

@inproceedings{
  title={Towards Robust and Expressive Whole-body Human Pose and Shape Estimation},
  author={Pang, Hui En and Cai, Zhongang and Yang, Lei and Qingyi, Tao and Zhonghua, Wu and Zhang, Tianwei and Liu, Ziwei},
  booktitle={NeurIPS},
  year={2023}
}

License

Distributed under the S-Lab License. See LICENSE for more information.

Acknowledgements

This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme. This study is also supported by the Ministry of Education, Singapore, under its MOE AcRF Tier 2 (MOE-T2EP20221-0012), NTU NAP, and under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). We sincerely thank the anonymous reviewers for their valuable comments on this paper.

robosmplx / RoboSMPLX