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)"

Last update: Jan 04, 2023

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)	Gait3D	16.50	31.10	11.71
Gait3D	GREW (official split)	18.81	32.25	~
Gait3D	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

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

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

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

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

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

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

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

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

Releases(v0.1)

v0.1(Mar 25, 2022)

Upload checkpoint models
Source code(tar.gz)
Source code(zip)
128pixel-Baseline-180000.pt(309.06 MB)
128pixel-GaitGL-180000.pt(309.76 MB)
128pixel-GaitPart-180000.pt(13.85 MB)
128pixel-GaitSet-180000.pt(29.70 MB)
128pixel-GLN_P2-180000.pt(176.87 MB)
128pixel-SMPLGait_128pixel-180000.pt(312.63 MB)
64pixel-Baseline-180000.pt(309.06 MB)
64pixel-GaitGL-180000.pt(309.76 MB)
64pixel-GaitPart-180000.pt(13.85 MB)
64pixel-GaitSet-180000.pt(29.70 MB)
64pixel-GLN_P2-180000.pt(176.87 MB)
64pixel-SMPLGait_64pixel-180000.pt(310.35 MB)

Owner

Official repo for Gait3D dataset

GitHub Repository

Contrastive Loss Gradient Attack (CLGA)

Contrastive Loss Gradient Attack (CLGA) Official implementation of Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation, WWW22 Bu

12 Dec 23, 2022

COCO Style Dataset Generator GUI

A simple GUI-based COCO-style JSON Polygon masks' annotation tool to facilitate quick and efficient crowd-sourced generation of annotation masks and bounding boxes. Optionally, one could choose to us

142 Dec 09, 2022

Keeper for Ricochet Protocol, implemented with Apache Airflow

Ricochet Keeper This repository contains Apache Airflow DAGs for executing keeper operations for Ricochet Exchange. Usage You will need to run this us

5 May 24, 2022

Efficient Sparse Attacks on Videos using Reinforcement Learning

EARL This repository provides a simple implementation of the work "Efficient Sparse Attacks on Videos using Reinforcement Learning" Example: Demo: Her

12 Dec 05, 2021

Code for "Long Range Probabilistic Forecasting in Time-Series using High Order Statistics"

Long Range Probabilistic Forecasting in Time-Series using High Order Statistics This is the code produced as part of the paper Long Range Probabilisti

16 Dec 06, 2022

Spatial Sparse Convolution Library

SpConv: Spatially Sparse Convolution Library PyPI Install Downloads CPU (Linux Only) pip install spconv CUDA 10.2 pip install spconv-cu102 CUDA 11.1 p

1.2k Jan 07, 2023

Self-Supervised Image Denoising via Iterative Data Refinement

Self-Supervised Image Denoising via Iterative Data Refinement Yi Zhang1, Dasong Li1, Ka Lung Law2, Xiaogang Wang1, Hongwei Qin2, Hongsheng Li1 1CUHK-S

72 Jan 01, 2023

Gesture Volume Control Using OpenCV and MediaPipe

This Project Uses OpenCV and MediaPipe Hand solutions to identify hands and Change system volume by taking thumb and index finger positions

6 Sep 12, 2022

This is a simple plugin for Vim that allows you to use OpenAI Codex.

🤖 Vim Codex An AI plugin that does the work for you. This is a simple plugin for Vim that will allow you to use OpenAI Codex. To use this plugin you

195 Dec 28, 2022

This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"

697 Jan 06, 2023