This repository holds the Pytorch implementation of Modulated Graph Convolutional Network for 3D Human Pose Estimation by Zhiming Zou and Wei Tang. If you find our code useful in your research, please consider citing:
@InProceedings{Zou_2021_ICCV,
author = {Zou, Zhiming and Tang, Wei},
title = {Modulated Graph Convolutional Network for 3D Human Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {11477-11487}
}
The proposed Modulated Graph Convolutional Network (Modulated GCN) is a graph convolutional network architecture that operates on regression tasks with graph-structured data. We evaluate our model for 3D human pose estimation on the Human3.6M Dataset.
In this repository, only 2D joints of the human pose are exploited as inputs. We utilize the method described in Pavllo et al. [2] to normalize 2D and 3D poses in the dataset. To be specific, 2D poses are scaled according to the image resolution and normalized to [-1, 1]; 3D poses are aligned with respect to the root joint. Please refer to the corresponding part in Pavllo et al. [2] for more details. For the 2D ground truth, we predict 16 joints (as the skeleton in Martinez et al. [1] and Zhao et al. [3] without the 'Neck/Nose' joint). For the 2D pose detections, the 'Neck/Nose' joint is reserved.
Under Protocol 1 (mean per-joint position error) and Protocol 2 (mean per-joint position error after rigid alignment).
| Method | 2D Detections | # of Epochs | # of Parameters | MPJPE (P1) | P-MPJPE (P2) |
|---|---|---|---|---|---|
| SemGCN | Ground truth | 50 | 0.27M | 42.14 mm | 33.53 mm |
| SemGCN (w/ Non-local) | Ground truth | 30 | 0.43M | 40.78 mm | 31.46 mm |
| Modulated GCN | Ground truth | 50 | 0.29M | 38.25 mm | 30.06 mm |
Results using Ground truth are reported. The results are borrowed from SemGCN.
This repository is build upon Python v3.7 and Pytorch v1.7.0 on Ubuntu 18.04. All experiments are conducted on a single NVIDIA RTX 2080 Ti GPU. See requirements.txt for other dependencies. We recommend installing Python v3.7 from Anaconda and installing Pytorch (>= 1.7.0) following guide on the official instructions according to your specific CUDA version. Then you can install dependencies with the following commands.
git clone https://github.com/ZhimingZo/Modulated-GCN.git
cd Modulated-GCN
pip install -r requirements.txt
CPN 2D detections for Human3.6M datasets are provided by VideoPose3D Pavllo et al. [2], which can be downloaded by the following steps:
cd dataset
wget https://dl.fbaipublicfiles.com/video-pose-3d/data_2d_h36m_cpn_ft_h36m_dbb.npz
wget https://dl.fbaipublicfiles.com/video-pose-3d/data_2d_h36m_detectron_ft_h36m.npz
cd ..
3D labels and ground truth can be downloaded
cd dataset
gdown --id 1P7W3ldx2lxaYJJYcf3RG4Y9PsD4EJ6b0
cd ..
The pre-trained models can be downloaded from Google Drive. Put ckpt in the project root directory.
To evaluate Modulated GCN with CPN detectors as input, run:
python main_graph.py --post_refine --module_gcn_reload 1 --previous_dir './ckpt/best_model' --save_dir './ckpt_best_model' --post_refine_reload 1 --nepoch 2 --show_protocol2
Note that the results will be reported in an action-wise manner.
If you want to reproduce the results of our pretrained models, run the following commands. For Modulated GCN:
python main_graph.py --pro_train 1 --save_model 1 --save_dir './ckpt'
By default the application runs in testing mode.
If you want to try different network settings, please refer to opt1.py for more details. Note that the
default setting of hyper-parameters is used for training model with CPN detectors as input, please refer to the paper for implementation details.
GT 2D keypoints for Human3.6M datasets are obtained from SemGCN Zhao et al. [3], which can be downloaded by the following steps:
cd data
pip install gdown
gdown https://drive.google.com/uc?id=1Ac-gUXAg-6UiwThJVaw6yw2151Bot3L1
python prepare_data_h36m.py --from-archive h36m.zip
cd ..
After this step, you should end up with two files in the dataset directory: data_3d_h36m.npz for the 3D poses, and data_2d_h36m_gt.npz for the ground-truth 2D poses.
python main_gcn.py --eva checkpoint/ckpt_Modulated_GCN_128_38.25_30.06.pth.tar
python main_gcn.py
[1] Martinez et al. A simple yet effective baseline for 3d human pose estimation. ICCV 2017.
[2] Pavllo et al. 3D human pose estimation in video with temporal convolutions and semi-supervised training. CVPR 2019.
[3] Zhao et al. Semantic Graph Convolutional Networks for 3D Human Pose Regression. CVPR 2019.
[3] Cai et al. Exploiting Spatial-temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks. ICCV 2019.
This code is extended from the following repositories.
We thank the authors for releasing their code. Please also consider citing their work.