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

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
  • Git
$ sudo apt install git

Data preparation

Download datasets:

  1. Campus (http://campar.in.tum.de/Chair/MultiHumanPose)
  2. Shelf (http://campar.in.tum.de/Chair/MultiHumanPose)
  3. 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

  1. YOLOv3
  2. HRNet

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}
}
PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC

DeepLab with PyTorch This is an unofficial PyTorch implementation of DeepLab v2 [1] with a ResNet-101 backbone. COCO-Stuff dataset [2] and PASCAL VOC

Kazuto Nakashima 995 Jan 08, 2023
这是一个deeplabv3-plus-pytorch的源码,可以用于训练自己的模型。

DeepLabv3+:Encoder-Decoder with Atrous Separable Convolution语义分割模型在Pytorch当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Download 训练步骤

Bubbliiiing 350 Dec 28, 2022
A forwarding MPI implementation that can use any other MPI implementation via an MPI ABI

MPItrampoline MPI wrapper library: MPI trampoline library: MPI integration tests: MPI is the de-facto standard for inter-node communication on HPC sys

Erik Schnetter 31 Dec 22, 2022
Bayesian optimization in PyTorch

BoTorch is a library for Bayesian Optimization built on PyTorch. BoTorch is currently in beta and under active development! Why BoTorch ? BoTorch Prov

2.5k Dec 31, 2022
Python Interview Questions

Python Interview Questions Clone the code to your computer. You need to understand the code in main.py and modify the content in if __name__ =='__main

ClassmateLin 575 Dec 28, 2022
PyTorch implementation of MuseMorphose, a Transformer-based model for music style transfer.

MuseMorphose This repository contains the official implementation of the following paper: Shih-Lun Wu, Yi-Hsuan Yang MuseMorphose: Full-Song and Fine-

Yating Music, Taiwan AI Labs 142 Jan 08, 2023
Training data extraction on GPT-2

Training data extraction from GPT-2 This repository contains code for extracting training data from GPT-2, following the approach outlined in the foll

Florian Tramer 62 Dec 07, 2022
Cache Requests in Deta Bases and Echo them with Deta Micros

Deta Echo Cache Leverage the awesome Deta Micros and Deta Base to cache requests and echo them as needed. Stop worrying about slow public APIs or agre

Gingerbreadfork 8 Dec 07, 2021
DRIFT is a tool for Diachronic Analysis of Scientific Literature.

About DRIFT is a tool for Diachronic Analysis of Scientific Literature. The application offers user-friendly and customizable utilities for two modes:

Rajaswa Patil 108 Dec 12, 2022
Revisiting Weakly Supervised Pre-Training of Visual Perception Models

SWAG: Supervised Weakly from hashtAGs This repository contains SWAG models from the paper Revisiting Weakly Supervised Pre-Training of Visual Percepti

Meta Research 134 Jan 05, 2023
[3DV 2020] PeeledHuman: Robust Shape Representation for Textured 3D Human Body Reconstruction

PeeledHuman: Robust Shape Representation for Textured 3D Human Body Reconstruction International Conference on 3D Vision, 2020 Sai Sagar Jinka1, Rohan

Rohan Chacko 39 Oct 12, 2022
A Joint Video and Image Encoder for End-to-End Retrieval

Frozen️ in Time ❄️ ️️️️ ⏳ A Joint Video and Image Encoder for End-to-End Retrieval project page | arXiv | webvid-data Repository containing the code,

225 Dec 25, 2022
Thermal Control of Laser Powder Bed Fusion using Deep Reinforcement Learning

This repository is the implementation of the paper "Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning", linked here. The project makes use of the Deep Reinforcement Library

BaratiLab 11 Dec 27, 2022
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~

YOLOv5-Lite:lighter, faster and easier to deploy Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, a

pogg 1.5k Jan 05, 2023
Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data

SEDE SEDE (Stack Exchange Data Explorer) is new dataset for Text-to-SQL tasks with more than 12,000 SQL queries and their natural language description

Rupert. 83 Nov 11, 2022
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

Densely Connected Convolutional Networks (DenseNets) This repository contains the code for DenseNet introduced in the following paper Densely Connecte

Zhuang Liu 4.5k Jan 03, 2023
A criticism of a recent paper on buggy image downsampling methods in popular image processing and deep learning libraries.

A criticism of a recent paper on buggy image downsampling methods in popular image processing and deep learning libraries.

70 Jul 12, 2022
Pytorch implementation of "Neural Wireframe Renderer: Learning Wireframe to Image Translations"

Neural Wireframe Renderer: Learning Wireframe to Image Translations Pytorch implementation of ideas from the paper Neural Wireframe Renderer: Learning

Yuan Xue 7 Nov 14, 2022
[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

TorchSemiSeg [CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision by Xiaokang Chen1, Yuhui Yuan2, Gang Zeng1, Jingdong Wang

Chen XiaoKang 387 Jan 08, 2023
Pyramid Scene Parsing Network, CVPR2017.

Pyramid Scene Parsing Network by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page. Introduction This

Hengshuang Zhao 1.5k Jan 05, 2023