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Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose

pose to mesh quality results

News

  • Update 21.04.27: Update PoseFix code and AMASS dataloader. Lowered PA-MPJPE, MPVPE on 3DPW!
  • Update 21.04.09: Update 3DPW evaluation code. Add temporal smoothing code and PA-MPVPE calculation code. They are commented for faster evaluation, but you can uncomment them in evaluate function of ${ROOT}/data/PW3D/dataset.py.
  • Update 21.04.09: Add demo on multiple people, and make a rendered mesh be overlayed on an input image
  • Update 20.11.016: Increased accuracy on 3DPW using DarkPose 2D pose outputs.

Introduction

This repository is the offical Pytorch implementation of Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose (ECCV 2020). Below is the overall pipeline of Pose2Mesh. overall pipeline

Install Guidelines

  • We recommend you to use an Anaconda virtual environment. Install PyTorch >= 1.2 according to your GPU driver and Python >= 3.7.2, and run sh requirements.sh.

Quick Demo

  • Download the pre-trained Pose2Mesh according to this.
  • Prepare SMPL and MANO layers according to this.
  • Prepare a pose input, for instance, as input.npy. input.npy should contain the coordinates of 2D human joints, which follow the topology of joint sets defined here. The joint orders can be found in each ${ROOT}/data/*/dataset.py.

Demo on a Single Person

  • Run python demo/run.py --gpu 0 --input_pose demo/h36m_joint_input.npy --joint_set human36.
  • You can replace demo/h36m_joint_input.npy and human36 with your input numpy file and one of {human36,coco,smpl,mano}.
  • Add --input_img {img_path} on the command if you want to a rendered mesh overlayed on an input image.
  • The outputs demo_pose2d.png, demo_mesh.png, and demo_mesh_.obj will be saved in ${ROOT}/demo/result/.

Demo on Multiple People

  • Download demo input from here and place them under ${ROOT}/demo/.
  • Run python demo/run.py --gpu 0.
  • Outputs on a sampled image from CrowdPose datasest will be saved in ${ROOT}/demo/result/.
  • You can change an input image and some details in lines 264~278 of ${ROOT}/demo/run.py.

Results

Here I report the performance of Pose2Mesh.

💪 Update: We increased the performance on 3DPW using GT meshes obtained from NeuralAnnot on COCO and AMASS. The annotations from NeuralAnnot are yet to be released.
💪 Update: The performance on 3DPW has increased using DarkPose 2D detection, which improved HRNet.

table

Below shows the results when the input is groundtruth 2D human poses. For Human3.6M benchmark, Pose2Mesh is trained on Human3.6M. For 3DPW benchmark, Pose2Mesh is trained on Human3.6M and COCO.

MPJPE PA-MPJPE
Human36M 51.28 mm 35.61 mm
3DPW 63.10 mm 35.37 mm

We provide qualitative results on SURREAL to show that Pose2Mesh can recover 3D shape to some degree. Please refer to the paper for more discussion.

surreal quality results

Directory

Root

The ${ROOT} is described as below.

${ROOT} 
|-- data
|-- demo
|-- lib
|-- experiment
|-- main
|-- manopth
|-- smplpytorch
  • data contains data loading codes and soft links to images and annotations directories.
  • demo contains demo codes.
  • lib contains kernel codes for Pose2Mesh.
  • main contains high-level codes for training or testing the network.
  • experiment contains the outputs of the system, whic include train logs, trained model weights, and visualized outputs.

Data

The data directory structure should follow the below hierarchy.

${ROOT}  
|-- data  
|   |-- Human36M  
|   |   |-- images  
|   |   |-- annotations   
|   |   |-- J_regressor_h36m_correct.npy
|   |   |-- absnet_output_on_testset.json 
|   |-- MuCo  
|   |   |-- data  
|   |   |   |-- augmented_set  
|   |   |   |-- unaugmented_set  
|   |   |   |-- MuCo-3DHP.json
|   |   |   |-- smpl_param.json
|   |-- COCO  
|   |   |-- images  
|   |   |   |-- train2017  
|   |   |   |-- val2017  
|   |   |-- annotations  
|   |   |-- J_regressor_coco.npy
|   |   |-- hrnet_output_on_valset.json
|   |-- PW3D 
|   |   |-- data
|   |   |   |-- 3DPW_latest_train.json
|   |   |   |-- 3DPW_latest_validation.json
|   |   |   |-- darkpose_3dpw_testset_output.json
|   |   |   |-- darkpose_3dpw_validationset_output.json
|   |   |-- imageFiles
|   |-- AMASS
|   |   |-- data
|   |   |   |-- cmu
|   |-- SURREAL
|   |   |-- data
|   |   |   |-- train.json
|   |   |   |-- val.json
|   |   |   |-- hrnet_output_on_testset.json
|   |   |   |-- simple_output_on_testset.json
|   |   |-- images
|   |   |   |-- train
|   |   |   |-- test
|   |   |   |-- val
|   |-- FreiHAND
|   |   |-- data
|   |   |   |-- training
|   |   |   |-- evaluation
|   |   |   |-- freihand_train_coco.json
|   |   |   |-- freihand_train_data.json
|   |   |   |-- freihand_eval_coco.json
|   |   |   |-- freihand_eval_data.json
|   |   |   |-- hrnet_output_on_testset.json
|   |   |   |-- simple_output_on_testset.json

If you have a problem with 'download limit' when trying to download datasets from google drive links, please try this trick.

  • Go the shared folder, which contains files you want to copy to your drive
  • Select all the files you want to copy
  • In the upper right corner click on three vertical dots and select “make a copy”
  • Then, the file is copied to your personal google drive account. You can download it from your personal account.

Pytorch SMPL and MANO layer

  • For the SMPL layer, I used smplpytorch. The repo is already included in ${ROOT}/smplpytorch.
  • Download basicModel_f_lbs_10_207_0_v1.0.0.pkl, basicModel_m_lbs_10_207_0_v1.0.0.pkl, and basicModel_neutral_lbs_10_207_0_v1.0.0.pkl from here (female & male) and here (neutral) to ${ROOT}/smplpytorch/smplpytorch/native/models. For the MANO layer, I used manopth. The repo is already included in ${ROOT}/manopth. Download MANO_RIGHT.pkl from here at ${ROOT}/manopth/mano/models.

Experiment

The experiment directory will be created as below.

${ROOT}  
|-- experiment  
|   |-- exp_*  
|   |   |-- checkpoint  
|   |   |-- graph 
|   |   |-- vis 
  • experiment contains train/test results of Pose2Mesh on various benchmark datasets. We recommed you to create the folder as a soft link to a directory with large storage capacity.

  • exp_* is created for each train/test command. The wildcard symbol refers to the time of the experiment train/test started. Default timezone is UTC+9, but you can set to your local time.

  • checkpoint contains the model checkpoints for each epoch.

  • graph contains visualized train logs of error and loss.

  • vis contains *.obj files of meshes and images with 2D human poses or human meshes.

Pretrained model weights

Download pretrained model weights from here to a corresponding directory.

${ROOT}  
|-- experiment  
|   |-- posenet_human36J_train_human36 
|   |-- posenet_cocoJ_train_human36_coco_muco
|   |-- posenet_smplJ_train_surreal
|   |-- posenet_manoJ_train_freihand
|   |-- pose2mesh_human36J_train_human36
|   |-- pose2mesh_cocoJ_train_human36_coco_muco
|   |-- pose2mesh_smplJ_train_surreal
|   |-- pose2mesh_manoJ_train_freihand
|   |-- posenet_human36J_gt_train_human36
|   |-- posenet_cocoJ_gt_train_human36_coco
|   |-- pose2mesh_human36J_gt_train_human36
|   |-- pose2mesh_cocoJ_gt_train_human36_coco

Running Pose2Mesh

joint set topology

Start

  • Pose2Mesh uses different joint sets from Human3.6M, COCO, SMPL, and MANO for Human3.6M, 3DPW, SURREAL, and FreiHAND benchmarks respectively. For the COCO joint set, we manually add 'Pelvis' and 'Neck' joints by computing the middle point of 'L_Hip' and 'R_Hip', and 'L_Shoulder' and 'R_Shoulder' respectively.
  • In the lib/core/config.py, you can change settings of the system including a train/test dataset to use, a pre-defined joint set, a pre-trained PoseNet, a learning schedule, GT usage, and so on.
  • Note that the first dataset on the DATASET.{train/test}_list should call build_coarse_graphs function for the graph convolution setting. Refer to the last line of __init__ function in ${ROOT}/data/Human36M/dataset.py.

Train

Select the config file in ${ROOT}/asset/yaml/ and train. You can change the train set and pretrained posenet by your own *.yml file.

1. Pre-train PoseNet

To train from the scratch, you should pre-train PoseNet first.

Run

python main/train.py --gpu 0,1,2,3 --cfg ./asset/yaml/posenet_{input joint set}_train_{dataset list}.yml

2. Train Pose2Mesh

Copy best.pth.tar in ${ROOT}/experiment/exp_*/checkpoint/ to ${ROOT}/experiment/posenet_{input joint set}_train_{dataset list}/. Or download the pretrained weights following this.

Run

python main/train.py --gpu 0,1,2,3 --cfg ./asset/yaml/pose2mesh_{input joint set}_train_{dataset list}.yml

Test

Select the config file in ${ROOT}/asset/yaml/ and test. You can change the pretrained model weight. To save sampled outputs to obj files, change TEST.vis value to True in the config file.

Run

python main/test.py --gpu 0,1,2,3 --cfg ./asset/yaml/{model name}_{input joint set}_test_{dataset name}.yml

Reference

@InProceedings{Choi_2020_ECCV_Pose2Mesh,  
author = {Choi, Hongsuk and Moon, Gyeongsik and Lee, Kyoung Mu},  
title = {Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose},  
booktitle = {European Conference on Computer Vision (ECCV)},  
year = {2020}  
}  

Related Projects

I2L-MeshNet_RELEASE
3DCrowdNet_RELEASE
TCMR_RELEASE
Hand4Whole_RELEASE
HandOccNet
NeuralAnnot_RELEASE

About

Official Pytorch implementation of "Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose", ECCV 2020

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