This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)"

Related tags

Deep Learningpythonpytorchgaitgait-recognitiongait-in-the-wildgait3dsmplgait
Overview

Gait3D-Benchmark

This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)". The official project page is here.

What's New

  • [Mar 2022] Another gait in the wild dataset GREW is supported.
  • [Mar 2022] Our Gait3D dataset and SMPLGait method are released.

Model Zoo

Gait3D

Input Size: 128x88(64x44)

Method [email protected] [email protected] mAP mINP download
GaitSet(AAAI2019)) 42.60(36.70) 63.10(58.30) 33.69(30.01) 19.69(17.30) model-128(model-64)
GaitPart(CVPR2020) 29.90(28.20) 50.60(47.60) 23.34(21.58) 13.15(12.36) model-128(model-64)
GLN(ECCV2020) 42.20(31.40) 64.50(52.90) 33.14(24.74) 19.56(13.58) model-128(model-64)
GaitGL(ICCV2021) 23.50(29.70) 38.50(48.50) 16.40(22.29) 9.20(13.26) model-128(model-64)
OpenGait Baseline* 47.70(42.90) 67.20(63.90) 37.62(35.19) 22.24(20.83) model-128(model-64)
SMPLGait(CVPR2022) 53.20(46.30) 71.00(64.50) 42.43(37.16) 25.97(22.23) model-128(model-64)

*It should be noticed that OpenGait Baseline is equal to SMPLGait w/o 3D in our paper.

Cross Domain

Datasets in the Wild (GaitSet, 64x44)

Source Target [email protected] [email protected] mAP
GREW (official split) Gait3D 15.80 30.20 11.83
GREW (our split) 16.50 31.10 11.71
Gait3D GREW (official split) 18.81 32.25 ~
GREW (our split) 43.86 60.89 28.06

Requirements

  • pytorch >= 1.6
  • torchvision
  • pyyaml
  • tensorboard
  • opencv-python
  • tqdm
  • py7zr
  • tabulate
  • termcolor

Installation

You can replace the second command from the bottom to install pytorch based on your CUDA version.

git clone https://github.com/Gait3D/Gait3D-Benchmark.git
cd Gait3D-Benchmark
conda create --name py37torch160 python=3.7
conda activate py37torch160
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
pip install tqdm pyyaml tensorboard opencv-python tqdm py7zr tabulate termcolor

Data Preparation

Please download the Gait3D dataset by signing an agreement. We ask for your information only to make sure the dataset is used for non-commercial purposes. We will not give it to any third party or publish it publicly anywhere.

Data Pretreatment

Run the following command to preprocess the Gait3D dataset.

python misc/pretreatment.py --input_path 'Gait3D/2D_Silhouettes' --output_path 'Gait3D-sils-64-44-pkl' --img_h 64 --img_w 44
python misc/pretreatment.py --input_path 'Gait3D/2D_Silhouettes' --output_path 'Gait3D-sils-128-88-pkl' --img_h 128 --img_w 88
python misc/pretreatment_smpl.py --input_path 'Gait3D/3D_SMPLs' --output_path 'Gait3D-smpls-pkl'

Data Structrue

After the pretreatment, the data structure under the directory should like this

├── Gait3D-sils-64-44-pkl
│  ├── 0000
│     ├── camid0_videoid2
│        ├── seq0
│           └──seq0.pkl
├── Gait3D-sils-128-88-pkl
│  ├── 0000
│     ├── camid0_videoid2
│        ├── seq0
│           └──seq0.pkl
├── Gait3D-smpls-pkl
│  ├── 0000
│     ├── camid0_videoid2
│        ├── seq0
│           └──seq0.pkl

Train

Run the following command:

sh train.sh

Test

Run the following command:

sh test.sh

Citation

Please cite this paper in your publications if it helps your research:

@inproceedings{zheng2022gait3d,
  title={Gait Recognition in the Wild with Dense 3D Representations and A Benchmark},
  author={Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Acknowledgement

Here are some great resources we benefit:

  • The codebase is based on OpenGait.
  • The 3D SMPL data is obtained by ROMP.
  • The 2D Silhouette data is obtained by HRNet-segmentation.
  • The 2D pose data is obtained by HRNet.
  • The ReID featrue used to make Gait3D is obtained by FastReID.
Comments
  • lib/modeling/models/smplgait.py throwing error when training a new dataset

    lib/modeling/models/smplgait.py throwing error when training a new dataset

    Hi Jinkai,

    When I try to use the SMPLGait to apply on other dataset, during the training process, the smplgait.py throws the error that: smpls = ipts[1][0] # [n, s, d] IndexError: list index out of range It is also interesting that I used 4 GPUs in the training. 3 of them could detect the the ipts[1][0] tensor with size 1. However, the fourth one failed to do so. Could I know how I can solve this?

    opened by zhiyuann 7
  • I have a few questions about Gait3D-Benchmark Datasets

    I have a few questions about Gait3D-Benchmark Datasets

    Hi. Im jjun. I read your paper impressively.

    We don't currently live in China, so it is difficult to use dataset on baidu disk.

    If you don't mind, is there a way to download the dataset to another disk (e.g Google drive)?

    opened by jjunnii 6
  • Question about 3D SMPL skeleton topology diagram

    Question about 3D SMPL skeleton topology diagram

    Your work promotes the application of gait recognition in real scenes, can you provide the topology diagram of the SMPL 3D skeleton in Gait3D? Because the specific meaning of the 24 joint points is not stated in your data description document.

    opened by HL-HYX 4
  • ROMP SMPL transfer

    ROMP SMPL transfer

    When I try to use the ROMP to generate out the 3D mesh, I detect there is a version conflict with the ROMP used by SMPLGait. Could I know which version of the ROMP the SMPLGait used? In this way I could use the SMPLGait to run on other ReID dataset.

    opened by zhiyuann 3
  • question about iteration and epoch

    question about iteration and epoch

    Hi! The total iteration in your code is set to 180000, and you report the total epoch as 1200 in your paper. What's the relationship between iteration and epoch?

    opened by yan811 2
  • About data generation

    About data generation

    Hi! I 'd like to know some details about data generation in NPZ files.

    In npz file: 1 What's the order of "pose"? SMPL pose parameter should be [24,3] dim, how did you convert it to [72,]? The order is [keypoint1_angel1, keypoint1_angle2, keypoint1_angle3, keypoint2_angel1, keypoint2_angle2, keypoint2_angle3...] or [keypoint1_angle1, keypoint2_angle1... keypoint1_angle2, keypoint2_angle2... keypoint1_angle3, keypoint1_angle3... ] ?

    2 How did you generate pose into SMPL format,SPIN format , and OpenPose format? What's the order of the second dim? Is the keypoint order the same with SMPL model?

    3 In pkl file: For example, dim of data in './0000/camid0_videoid2/seq0/seq0.pkl' is [48,85]. What's the order of dim 1? Is it ordered by time order or shuffled?

    opened by yan811 2
  • GREW pretreatment `to_pickle` has size 0

    GREW pretreatment `to_pickle` has size 0

    I'm trying to run GREW pretreatment code but it generates no GREW-pkl folder at the end of the process. I debugged myself and checked if the --dataset flag is set properly and the to_pickle list size before saving the pickle file. The flag is well set but the size of the list is always 0.

    I downloaded the GREW dataset from the link you guys sent me and made de GREW-rearranged folder using the code provided. I'll keep investigating what is causing such an error and if I find I'll set a fixing PR.

    opened by gosiqueira 1
  • About the pose data

    About the pose data

    Can you make a detailed description of the pose data? This is the path of one frame pose and the corresponding content of the txt file Gait3D/2D_Poses/0000/camid9_videoid2/seq0/human_crop_f17279.txt '311,438,89.201164,62.87694,0.57074964,89.201164,54.322254,0.47146344,84.92382,62.87694,0.63443935,42.150383....' I have 3 questions. Q1: what does 'f17279' means? Q2: what does the first number (e.g. 311) in the txt file mean? Q3: which number('f17279' or '311') should I regard as a base when I order the sequence? Thank you very much!

    opened by HiAleeYang 0
Owner
Official repo for Gait3D dataset
GitHub Repository
Offline Reinforcement Learning with Implicit Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning This repository contains the official implementation of Offline Reinforcement Learning with Im

Ilya Kostrikov 125 Dec 31, 2022
Retrieve and analysis data from SDSS (Sloan Digital Sky Survey)

Author: Behrouz Safari License: MIT sdss A python package for retrieving and analysing data from SDSS (Sloan Digital Sky Survey) Installation Install

Behrouz 3 Oct 28, 2022
[NeurIPS 2020] Blind Video Temporal Consistency via Deep Video Prior

pytorch-deep-video-prior (DVP) Official PyTorch implementation for NeurIPS 2020 paper: Blind Video Temporal Consistency via Deep Video Prior TensorFlo

Yazhou XING 90 Oct 19, 2022
Train Dense Passage Retriever (DPR) with a single GPU

Gradient Cached Dense Passage Retrieval Gradient Cached Dense Passage Retrieval (GC-DPR) - is an extension of the original DPR library. We introduce G

Luyu Gao 92 Jan 02, 2023
Production First and Production Ready End-to-End Speech Recognition Toolkit

WeNet 中文版 Discussions | Docs | Papers | Runtime (x86) | Runtime (android) | Pretrained Models We share neural Net together. The main motivation of WeN

2.7k Jan 04, 2023
The final project of "Applying AI to EHR Data" of "AI for Healthcare" nanodegree - Udacity.

Patient Selection for Diabetes Drug Testing Project Overview EHR data is becoming a key source of real-world evidence (RWE) for the pharmaceutical ind

Omar Laham 1 Jan 14, 2022
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

Pytorch Pedestrian Attribute Recognition: A strong PyTorch baseline of pedestrian attribute recognition and multi-label classification.

Jian 79 Dec 18, 2022
Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

Photo-Realistic-Super-Resoluton Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" [Paper]

Harry Yang 199 Dec 01, 2022
Visualizing lattice vibration information from phonon dispersion to atoms (For GPUMD)

Phonon-Vibration-Viewer (For GPUMD) Visualizing lattice vibration information from phonon dispersion for primitive atoms. In this tutorial, we will in

Liangting 6 Dec 10, 2022
Testing and Estimation of structural breaks in Stata

xtbreak estimating and testing for many known and unknown structural breaks in time series and panel data. For an overview of xtbreak test see xtbreak

Jan Ditzen 13 Jun 19, 2022
A machine learning project which can detect and predict the skin disease through image recognition.

ML-Project-2021 A machine learning project which can detect and predict the skin disease through image recognition. The dataset used for this is the H

Debshishu Ghosh 1 Jan 13, 2022
Code for BMVC2021 "MOS: A Low Latency and Lightweight Framework for Face Detection, Landmark Localization, and Head Pose Estimation"

MOS-Multi-Task-Face-Detect Introduction This repo is the official implementation of "MOS: A Low Latency and Lightweight Framework for Face Detection,

104 Dec 08, 2022
smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the COVID-19 case by Storvik et al

smc.covid smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectiou

0 Oct 15, 2021
AsymmetricGAN - Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

AsymmetricGAN for Image-to-Image Translation AsymmetricGAN Framework for Multi-Domain Image-to-Image Translation AsymmetricGAN Framework for Hand Gest

Hao Tang 42 Jan 15, 2022
Differentiable scientific computing library

xitorch: differentiable scientific computing library xitorch is a PyTorch-based library of differentiable functions and functionals that can be widely

98 Dec 26, 2022
Code for "The Box Size Confidence Bias Harms Your Object Detector"

The Box Size Confidence Bias Harms Your Object Detector - Code Disclaimer: This repository is for research purposes only. It is designed to maintain r

Johannes G. 24 Dec 07, 2022
Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

1 Nov 27, 2021
Stacs-ci - A set of modules to enable integration of STACS with commonly used CI / CD systems

Static Token And Credential Scanner CI Integrations What is it? STACS is a YARA

STACS 18 Aug 04, 2022
Pretraining Representations For Data-Efficient Reinforcement Learning

Pretraining Representations For Data-Efficient Reinforcement Learning Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Ch

Mila 40 Dec 11, 2022
Implementation of the paper "Generating Symbolic Reasoning Problems with Transformer GANs"

Generating Symbolic Reasoning Problems with Transformer GANs This is the implementation of the paper Generating Symbolic Reasoning Problems with Trans

Reactive Systems Group 1 Apr 18, 2022