Integral-Human-Pose-Regression-for-3D-Human-Pose-Estimation

Introduction

This repo is PyTorch implementation of Integral Human Pose Regression (ECCV 2018) of MSRA for 3D human pose estimation from a single RGB image.

What this repo provides:

• PyTorch implementation of Integral Human Pose Regression.
• Flexible and simple code.
• Dataset pre-processing codes for MPII and Human3.6M dataset.

Dependencies

• PyTorch
• CUDA
• cuDNN
• Anaconda
• COCO API

This code is tested under Ubuntu 16.04, CUDA 9.0, cuDNN 7.1 environment with two NVIDIA 1080Ti GPUs.

Python 3.6.5 version with Anaconda 3 and PyTorch 1.0.0 is used for development.

Directory

Root

The ${POSE_ROOT} is described as below.

${POSE_ROOT}
|-- data
|-- common
|-- main
|-- tool
`-- output

• data contains data loading codes and soft links to images and annotations directories.
• common contains kernel codes for 3d human pose estimation system.
• main contains high-level codes for training or testing the network.
• tool contains Human3.6M dataset preprocessing code.
• output contains log, trained models, visualized outputs, and test result.

Data

You need to follow directory structure of the data as below.

${POSE_ROOT}
|-- data
|-- |-- MPII
|   `-- |-- annotations
|       |   |-- train.json
|       |   `-- test.json
|       `-- images
|           |-- 000001163.jpg
|           |-- 000003072.jpg
|-- |-- Human36M
|   `-- |-- data
|       |   |-- s_01_act_02_subact_01_ca_01
|       |   |-- s_01_act_02_subact_01_ca_02

• In the tool, run preprocess_h36m.m to preprocess Human3.6M dataset. It converts videos to images and save meta data for each frame. data in Human36M contains the preprocessed data.
• Use MPII dataset preprocessing code in my TF-SimpleHumanPose git repo
• You can change default directory structure of data by modifying $DATASET_NAME.py of each dataset folder.

Output

You need to follow the directory structure of the output folder as below.

${POSE_ROOT}
|-- output
|-- |-- log
|-- |-- model_dump
|-- |-- result
`-- |-- vis

• Creating output folder as soft link form is recommended instead of folder form because it would take large storage capacity.
• log folder contains training log file.
• model_dump folder contains saved checkpoints for each epoch.
• result folder contains final estimation files generated in the testing stage.
• vis folder contains visualized results.
• You can change default directory structure of output by modifying main/config.py.

Running code

Start

• In the main/config.py, you can change settings of the model including dataset to use, network backbone, and input size and so on.

Train

In the main folder, set training set in config.py. Note that trainset must be list type and 0th dataset is the reference dataset.

In the main folder, run

python train.py --gpu 0-1

to train the network on the GPU 0,1.

If you want to continue experiment, run

python train.py --gpu 0-1 --continue

--gpu 0,1 can be used instead of --gpu 0-1.

Test

In the main folder, set testing set in config.py. Note that testset must be str type.

Place trained model at the output/model_dump/.

In the main folder, run

python test.py --gpu 0-1 --test_epoch 16

to test the network on the GPU 0,1 with 16th epoch trained model. --gpu 0,1 can be used instead of --gpu 0-1.

Results

Here I report the performance of the model from this repo and the original paper. Also, I provide pre-trained 3d human pose estimation models.

Results on Human3.6M dataset

The tables below are PA MPJPE and MPJPE on Human3.6M dataset. Provided config.py file is used to achieve below results. It's currently slightly worse than the performance of the original paper, however I'm trying to achieve the same performance. I think training schedule has to be changed.

Protocol 1 (training subjects: 1,5,6,7,8,9, testing subjects: 11)

Protocol 1 model and result will be available soon!!

Protocol 2 (training subjects: 1,5,6,7,8, testing subjects: 9, 11), PA MPJPE

The PA MPJPEs of the paper are from protocol 1, however, note that protocol 2 uses smaller training set.

Methods	Dir.	Dis.	Eat	Gre.	Phon.	Pose	Pur.	Sit.	Sit D.	Smo.	Phot.	Wait	Walk	Walk D.	Walk P.	Avg
my repo	39.0	38.6	44.1	42.5	40.6	35.3	38.2	49.9	59.4	41.00	46.1	37.6	30.3	40.8	35.5	41.5
original paper	36.9	36.2	40.6	40.4	41.9	34.9	35.7	50.1	59.4	40.4	44.9	39.0	30.8	39.8	36.7	40.6

Protocol 2 (training subjects: 1,5,6,7,8, testing subjects: 9, 11), MPJPE

Methods	Dir.	Dis.	Eat	Gre.	Phon.	Pose	Pur.	Sit.	Sit D.	Smo.	Phot.	Wait	Walk	Walk D.	Walk P.	Avg
my repo	50.8	52.3	54.8	57.9	52.8	47.0	52.1	62.0	73.7	52.6	58.3	50.4	40.9	54.1	45.1	53.9
original paper	47.5	47.7	49.5	50.2	51.4	43.8	46.4	58.9	65.7	49.4	55.8	47.8	38.9	49.0	43.8	49.6

• Pre-trained model of protocol 2 [model]

Troubleshooting

If you get an extremely large error, disable cudnn for batch normalization. This typically occurs in low version of PyTorch.

# PYTORCH=/path/to/pytorch
# for pytorch v0.4.0
sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
# for pytorch v0.4.1
sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py

Acknowledgement

This repo is largely modified from Original PyTorch repo of IntegralHumanPose.

Reference

[1] Sun, Xiao and Xiao, Bin and Liang, Shuang and Wei, Yichen. "Integral human pose regression". ECCV 2018.