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DiffPose: Toward More Reliable 3D Pose Estimation, CVPR2023

¹JIA GONG*, ¹Lin Geng Foo*, ²Zhipeng Fan, ³Qiuhong Ke, ⁴Hossein Rahmani, ¹Jun Liu,

* equal contribution

¹Singapore University of Technology and Design, ²New York University, ³Monash University, ⁴Lancaster University

[Paper] | [Project Page] | [SUTD-VLG Lab]

DiffPose Model Architecture

DiffPose Diffusion Process

Our code is built on top of DDIM.

Environment

The code is developed and tested under the following environment:

• Python 3.8.2
• PyTorch 1.7.1
• CUDA 11.0

You can create the environment via:

conda env create -f environment.yml

Dataset

Our datasets are based on 3d-pose-baseline and Video3D data. We provide the GMM format data generated from the above datasets here. You should put the downloaded files into the ./data directory. Note that we only change the format of the Video3D data to make them compatible with our GMM-based DiffPose training strategy, and the value of the 2D pose in our dataset is the same as them.

Frame-based experiments

Evaluating pre-trained models for frame-based experiments

We provide the pre-trained diffusion model (with CPN-dected 2D Pose as input) here. To evaluate it, put it into the ./checkpoint directory and run:

CUDA_VISIBLE_DEVICES=0 python main_diffpose_frame.py \
--config human36m_diffpose_uvxyz_cpn.yml --batch_size 1024 \
--model_pose_path checkpoints/gcn_xyz_cpn.pth \
--model_diff_path checkpoints/diffpose_uvxyz_cpn.pth \
--doc t_human36m_diffpose_uvxyz_cpn --exp exp --ni \
>exp/t_human36m_diffpose_uvxyz_cpn.out 2>&1 &

We also provide the pre-trained diffusion model (with Ground truth 2D pose as input) here. To evaluate it, put it into the ./checkpoint directory and run:

CUDA_VISIBLE_DEVICES=0 python main_diffpose_frame.py \
--config human36m_diffpose_uvxyz_gt.yml --batch_size 1024 \
--model_pose_path checkpoints/gcn_xyz_gt.pth \
--model_diff_path checkpoints/diffpose_uvxyz_gt.pth \
--doc t_human36m_diffpose_uvxyz_gt --exp exp --ni \
>exp/t_human36m_diffpose_uvxyz_gt.out 2>&1 &

Training new models

• To train a model from scratch (CPN 2D pose as input), run:

CUDA_VISIBLE_DEVICES=0 python main_diffpose_frame.py --train \
--config human36m_diffpose_uvxyz_cpn.yml --batch_size 1024 \
--model_pose_path checkpoints/gcn_xyz_cpn.pth \
--doc human36m_diffpose_uvxyz_cpn --exp exp --ni \
>exp/human36m_diffpose_uvxyz_cpn.out 2>&1 &

• To train a model from scratch (Ground truth 2D pose as input), run:

CUDA_VISIBLE_DEVICES=0 python main_diffpose_frame.py --train \
--config human36m_diffpose_uvxyz_gt.yml --batch_size 1024 \
--model_pose_path checkpoints/gcn_xyz_gt.pth \
--doc human36m_diffpose_uvxyz_gt --exp exp --ni \
>exp/human36m_diffpose_uvxyz_gt.out 2>&1 &

Video-based experiments

Refer to https://github.com/GONGJIA0208/Diffpose_video

Bibtex

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

@InProceedings{gong2023diffpose,
    author    = {Gong, Jia and Foo, Lin Geng and Fan, Zhipeng and Ke, Qiuhong and Rahmani, Hossein and Liu, Jun},
    title     = {DiffPose: Toward More Reliable 3D Pose Estimation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
}

Acknowledgement

Part of our code is borrowed from DDIM, VideoPose3D, Graformer, MixSTE and PoseFormer. We thank the authors for releasing the codes.