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The Pytorch implementation for "Semantic Graph Convolutional Networks for 3D Human Pose Regression" (CVPR 2019).
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README.md

Semantic Graph Convolutional Networks for 3D Human Pose Regression (CVPR 2019)

This repository holds the Pytorch implementation of Semantic Graph Convolutional Networks for 3D Human Pose Regression by Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia and Dimitris N. Metaxas. If you find our code useful in your research, please consider citing:

@inproceedings{zhaoCVPR19semantic,
  author    = {Zhao, Long and Peng, Xi and Tian, Yu and Kapadia, Mubbasir and Metaxas, Dimitris N.},
  title     = {Semantic Graph Convolutional Networks for 3D Human Pose Regression},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages     = {3425--3435},
  year      = {2019}
}

Introduction

We propose Semantic Graph Convolutional Networks (SemGCN), a novel graph convolutional network architecture that operates on regression tasks with graph-structured data. The code of training and evaluating our approach for 3D human pose estimation on the Human3.6M Dataset is provided in this repository.

In this repository, 3D human poses are predicted according to Configuration #1 in our paper: we only leverage 2D joints of the human pose as inputs. We utilize the method described in Pavllo et al. [2] to normalize 2D and 3D poses in the dataset, which is different from the original implementation in our paper. 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. We predict 16 joints (as the skeleton in Martinez et al. [1] without the 'Neck/Nose' joint). We also provide the results of Martinez et al. [1] in the same setting for comparison.

Results on Human3.6M

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)
Martinez et al. [1] Ground truth 200 4.29M 44.40 mm 35.25 mm
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
Martinez et al. [1] SH (fine-tuned) 200 4.29M 63.48 mm 48.15 mm
SemGCN (w/ Non-local) SH (fine-tuned) 100 0.43M 61.24 mm 47.71 mm

Results using two different 2D detections (Ground truth and Stacked Hourglass detections fine-tuned on Human3.6M) are reported.

References

[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.

Quick start

This repository is build upon Python v2.7 and Pytorch v1.1.0 on Ubuntu 16.04. NVIDIA GPUs are needed to train and test. See requirements.txt for other dependencies. We recommend installing Python v2.7 from Anaconda, and installing Pytorch (>= 1.1.0) following guide on the official instructions according to your specific CUDA version. Then you can install dependencies with the following commands.

git clone git@github.com:garyzhao/SemGCN.git
cd SemGCN
pip install -r requirements.txt

Dataset setup

You can find the instructions for setting up the Human3.6M and results of 2D detections in data/README.md. The code for data preparation is borrowed from VideoPose3D.

Evaluating our pretrained models

The pretrained models can be downloaded from Google Drive. Put checkpoint in the project root directory.

To evaluate Martinez et al. [1], run:

python main_linear.py --evaluate checkpoint/pretrained/ckpt_linear.pth.tar
python main_linear.py --evaluate checkpoint/pretrained/ckpt_linear_sh.pth.tar --keypoints sh_ft_h36m

To evaluate SemGCN without non-local blocks, run:

python main_gcn.py --evaluate checkpoint/pretrained/ckpt_semgcn.pth.tar

To evaluate SemGCN with non-local blocks, run:

python main_gcn.py --non_local --evaluate checkpoint/pretrained/ckpt_semgcn_nonlocal.pth.tar
python main_gcn.py --non_local --evaluate checkpoint/pretrained/ckpt_semgcn_nonlocal_sh.pth.tar --keypoints sh_ft_h36m

Note that the error is calculated in an action-wise manner.

Training from scratch

If you want to reproduce the results of our pretrained models, run the following commands.

For Martinez et al. [1]:

python main_linear.py

For SemGCN without non-local blocks:

python main_gcn.py --epochs 50

By default the application runs in training mode. This will train a new model for 50 epochs without non-local blocks, using ground truth 2D detections. You may change the value of num_layers (4 by default) and hid_dim (128 by default) if you want to try different network settings. Please refer to main_gcn.py for more details.

For SemGCN with non-local blocks:

python main_gcn.py --non_local --epochs 30

This will train a new model with non-local blocks for 30 epochs, using ground truth 2D detections.

For training and evaluating models using 2D detections generated by Stacked Hourglass, add --keypoints sh_ft_h36m to the commands:

python main_gcn.py --non_local --epochs 100 --keypoints sh_ft_h36m
python main_gcn.py --non_local --evaluate ${CHECKPOINT_PATH} --keypoints sh_ft_h36m

Visualization

You can generate visualizations of the model predictions by running:

python viz.py --architecture gcn --non_local --evaluate checkpoint/pretrained/ckpt_semgcn_nonlocal.pth.tar --viz_subject S11 --viz_action Walking --viz_camera 0 --viz_output output.gif --viz_size 3 --viz_downsample 2 --viz_limit 60

The script can also export MP4 videos, and supports a variety of parameters (e.g. downsampling/FPS, size, bitrate). See viz.py for more details.

Acknowledgement

Part of our code is borrowed from the following repositories.

We thank to the authors for releasing their codes. Please also consider citing their works.

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