RootNet of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image"

Introduction

This repo is official PyTorch implementation of Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image (ICCV 2019). It contains RootNet part.

What this repo provides:

• PyTorch implementation of Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image (ICCV 2019).
• Flexible and simple code.
• Compatibility for most of the publicly available 2D and 3D, single and multi-person pose estimation datasets including Human3.6M, MPII, MS COCO 2017, MuCo-3DHP, MuPoTS-3D, and 3DPW.
• Human pose estimation visualization code.

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 is used for development.

Quick demo

You can try quick demo at demo folder.

• Download the pre-trained RootNet in here.
• Prepare input.jpg and pre-trained snapshot at demo folder.
• Set bbox_list at here.
• Run python demo.py --gpu 0 --test_epoch 18 if you want to run on gpu 0.
• You can see output_root_2d.jpg and printed root joint depths.

Directory

Root

The ${POSE_ROOT} is described as below.

${POSE_ROOT}
|-- data
|-- demo
|-- common
|-- main
|-- output

• data contains data loading codes and soft links to images and annotations directories.
• demo contains demo codes.
• common contains kernel codes for 3d multi-person pose estimation system.
• main contains high-level codes for training or testing the network.
• 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
|   |-- Human36M
|   |   |-- bbox
|   |   |   |-- bbox_human36m_output.json
|   |   |-- images
|   |   |-- annotations
|   |-- MPII
|   |   |-- images
|   |   |-- annotations
|   |-- MSCOCO
|   |   |-- images
|   |   |   |-- train2017
|   |   |   |-- val2017
|   |   |-- annotations
|   |-- MuCo
|   |   |-- data
|   |   |   |-- augmented_set
|   |   |   |-- unaugmented_set
|   |   |   |-- MuCo-3DHP.json
|   |-- MuPoTS
|   |   |-- bbox
|   |   |   |-- bbox_mupots_output.json
|   |   |-- data
|   |   |   |-- MultiPersonTestSet
|   |   |   |-- MuPoTS-3D.json
|   |-- PW3D
|   |   |-- data
|   |   |   |-- 3DPW_train.json
|   |   |   |-- 3DPW_validation.json
|   |   |   |-- 3DPW_test.json
|   |   |-- imageFiles

• Download Human3.6M parsed data [data]
• Download MPII parsed data [images][annotations]
• Download MuCo parsed and composited data [data]
• Download MuPoTS parsed data [images][annotations]
• Download 3DPW parsed data [images][annotations]
• All annotation files follow MS COCO format.
• If you want to add your own dataset, you have to convert it to MS COCO format.

To download multiple files from Google drive without compressing them, try this. If you have a problem with 'Download limit' problem when tried to download dataset from google drive link, 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.  

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.

Running 3DMPPE_ROOTNET

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.
• YOU MUST SET bbox_real according to unit of each dataset. For example, Human3.6M uses milimeter, therefore bbox_real = (2000, 2000). 3DPW uses meter, therefore bbox_real = (2, 2).

Train

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

Place trained model at the output/model_dump/.

In the main folder, run

python test.py --gpu 0-1 --test_epoch 20

to test the network on the GPU 0,1 with 20th epoch trained model. --gpu 0,1 can be used instead of --gpu 0-1.

Results

• Pre-trained model of RootNet in here.
• Bounding boxs (from DetectNet and not extended) of Human3.6M and MuPoTS-3D datasets in here. You can use this to test RootNet.
• Bounding boxs (from DetectNet and extended) and root joint coordinates (from RootNet) of Human3.6M, MSCOCO, and MuPoTS-3D datasets in here. You should not use the bounding boxs of this file to test RootNet because the boxs are extended. Please use the right above one (bounding boxs from DetectNet without bbox extension).
• Bounding boxs (GT) and root joint coordinates (from RootNet) of 3DPW dataset (only test set) in here. The result is obtained from RootNet trained on MuCo+MSCOCO (without 3DPW training set).

For the evaluation, you can run test.py or there are evaluation codes in Human36M and MuPoTS.

Human3.6M dataset using protocol 2 (milimeter)

MuPoTS-3D dataset (percentage)

3DPW dataset (test set. meter)

MSCOCO dataset

We additionally provide estimated 3D human root coordinates in on the MSCOCO dataset. The coordinates are in 3D camera coordinate system, and focal lengths are set to 1500mm for both x and y axis. You can change focal length and corresponding distance using equation 2 or equation in supplementarial material of my paper.

Reference

@InProceedings{Moon_2019_ICCV_3DMPPE,
author = {Moon, Gyeongsik and Chang, Juyong and Lee, Kyoung Mu},
title = {Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image},
booktitle = {The IEEE Conference on International Conference on Computer Vision (ICCV)},
year = {2019}
}