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

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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

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.

Directory

Root

The ${POSE_ROOT} is described as below.

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

data contains data loading codes and soft links to images and annotations directories.
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
|       |   |-- train/
|       |   |-- val/
|       `-- 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.

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 root joint coordinates (from RootNet) of Human3.6M, MSCOCO, and MuPoTS-3D datasets in here.
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)

Method	MRPE	MRPE_x	MRPE_y	MRPE_z
RootNet	120.0	23.3	23.0	108.1

MuPoTS-3D dataset (milimeter)

Method	AP_25
RootNet	31.0

3DPW dataset (test set. meter)

Method	MRPE	MRPE_x	MRPE_y	MRPE_z
RootNet	0.386	0.045	0.094	0.353

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}
}