Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

Last update: Oct 27, 2022

Overview

Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking
Hau Chu, Jia-Hong Lee, Yao-Chih Lee, Ching-Hsien Hsu, Jia-Da Li, Chu-Song Chen
2021 CVPR B-AMFG Workshop

Note: It's a project of AI^2 Lab. The code will be update in here while there is a new version.

Installation

Python 3.6+
Cuda 9.0
Cudnn 7
gcc 5 & g++ 5 (for Ubuntu 18.04)

$ sudo apt install gcc-5 g++-5
$ sudo ln -s /usr/bin/gcc-6 /usr/local/bin/gcc
$ sudo ln -s /usr/bin/g++-6 /usr/local/bin/g++

Conda Env

$ conda create -n venv python=3.6
$ conda activate venv
$ conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=9.0 -c pytorch
$ pip install tensorflow_gpu==1.9.0
$ pip install -r requirements.txt

$ sudo apt install git

Data preparation

Download datasets:

Campus (http://campar.in.tum.de/Chair/MultiHumanPose)
Shelf (http://campar.in.tum.de/Chair/MultiHumanPose)
CMU Panoptic (https://github.com/CMU-Perceptual-Computing-Lab/panoptic-toolbox)

Dataset's camera_parameter.pickle download

The directory tree should look like below:

${ROOT}
    |-- CatchImage
        |-- CampusSeq1
        |   |-- Camera0
        |   |-- Camera1
        |   |-- Camera2
        |   |-- camera_parameter.pickle
        |   |-- actorsGT.mat
        |-- Shelf
        |   |-- Camera0
        |   |-- ...
        |   |-- Camera4
        |   |-- camera_parameter.pickle
        |   |-- actorsGT.mat
        |-- Panoptic
        |   |-- 160906_pizza1
            |   |-- 00_03 # hdImgs folder of 03 camera
            |   |-- 00_06 # hdImgs folder of 06 camera
            |   |-- ...
            |   |-- camera_parameter.pickle
            |   |-- hdPose_stage1_coco19
            |-- ...
    |-- src

Backend Models

Backend models, which is not our works, are released codes from others. We only did some small modifications to fit the format of our input/output. Put models in {ROOT}/src/backend

YOLOv3
- origin: https://github.com/ZQPei/deep_sort_pytorch
- modification
HRNet
- origin: https://github.com/stefanopini/simple-HRNet
- modification

Run Codes

Demo

$cd src
python -W ignore testmodel.py --dataset CampusSeq1 # For Campus
python -W ignore testmodel.py --dataset Shelf # For Shelf
python -W ignore testmodel.py --dataset Panoptic # For Panoptic (sub-dataset can be modified in config)

Evaluation

$cd src
python -W ignore evalmodel.py --dataset CampusSeq1 
python -W ignore evalmodel.py --dataset Shelf

Campus PCP Score

Bone Group	Actor 0	Actor 1	Actor 2	Average
Head	100.00	100.00	100.00	100.00
Torso	100.00	100.00	100.00	100.00
Upper arms	98.98	100.00	100.00	99.66
Lower arms	92.86	68.78	91.30	84.31
Upper legs	100.00	100.00	100.00	100.00
Lower legs	100.00	100.00	100.00	100.00
Total	98.37	93.76	98.26	96.79

Shelf PCP Score

Bone Group	Actor 0	Actor 1	Actor 2	Average
Head	94.98	100.00	91.30	95.43
Torso	100.00	100.00	100.00	100.00
Upper arms	100.00	100.00	96.27	98.76
Lower arms	98.21	77.03	96.27	90.50
Upper legs	100.00	100.00	100.00	100.00
Lower legs	100.00	100.00	100.00	100.00
Total	99.14	95.41	97.64	97.39

Citation

@InProceedings{Chu_2021_CVPR,
    author    = {Chu, Hau and Lee, Jia-Hong and Lee, Yao-Chih and Hsu, Ching-Hsien and Li, Jia-Da and Chen, Chu-Song},
    title     = {Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2021},
    pages     = {1472-1481}
}

Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

Related tags

Overview

Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

Installation

Data preparation

Backend Models

Run Codes

Demo

Evaluation

Campus PCP Score

Shelf PCP Score

Citation

Owner

Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains

Fre-GAN: Adversarial Frequency-consistent Audio Synthesis

MNIST, but with Bezier curves instead of pixels

Improving Non-autoregressive Generation with Mixup Training

[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

Classification models 1D Zoo - Keras and TF.Keras

Points2Surf: Learning Implicit Surfaces from Point Clouds (ECCV 2020 Spotlight)

Implementation of Research Paper "Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation"

Generating Videos with Scene Dynamics

Least Square Calibration for Peer Reviews

PyTorch implementation of Wide Residual Networks with 1-bit weights by McDonnell (ICLR 2018)

Implement A3C for Mujoco gym envs

Curated list of awesome GAN applications and demo

[ICLR2021] Unlearnable Examples: Making Personal Data Unexploitable

Unofficial PyTorch Implementation of Multi-Singer

Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling

State of the Art Neural Networks for Generative Deep Learning

A mini lib that implements several useful functions binding to PyTorch in C++.

EfficientNetV2-with-TPU - Cifar-10 case study

Learning Skeletal Articulations with Neural Blend Shapes