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
Implementation of Diverse Semantic Image Synthesis via Probability Distribution Modeling

Diverse Semantic Image Synthesis via Probability Distribution Modeling (CVPR 2021) Paper Zhentao Tan, Menglei Chai, Dongdong Chen, Jing Liao, Qi Chu,

tzt 45 Nov 17, 2022
Implementation of Sequence Generative Adversarial Nets with Policy Gradient

SeqGAN Requirements: Tensorflow r1.0.1 Python 2.7 CUDA 7.5+ (For GPU) Introduction Apply Generative Adversarial Nets to generating sequences of discre

Lantao Yu 2k Dec 29, 2022
MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation

MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation This repo is the official implementation of "MHFormer: Multi-Hypothesis Transforme

Vegetabird 281 Jan 07, 2023
Repository providing a wide range of self-supervised pretrained models for computer vision tasks.

Hierarchical Pretraining: Research Repository This is a research repository for reproducing the results from the project "Self-supervised pretraining

Colorado Reed 53 Nov 09, 2022
XViT - Space-time Mixing Attention for Video Transformer

XViT - Space-time Mixing Attention for Video Transformer This is the official implementation of the XViT paper: @inproceedings{bulat2021space, title

Adrian Bulat 33 Dec 23, 2022
Face recognition project by matching the features extracted using SIFT.

MV_FaceDetectionWithSIFT Face recognition project by matching the features extracted using SIFT. By : Aria Radmehr Professor : Ali Amiri Dependencies

Aria Radmehr 4 May 31, 2022
The official repo for CVPR2021——ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search [paper] Introduction This is the official implementation of ViPNAS: Efficient V

Lumin 42 Sep 26, 2022
Mail classification with tensorflow and MS Exchange Server (ham or spam).

Mail classification with tensorflow and MS Exchange Server (ham or spam).

Metin Karatas 1 Sep 11, 2021
FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

LST-TTS Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audi

Li-Wei Chen 64 Dec 30, 2022
A python package simulating the quasi-2D pseudospin-1/2 Gross-Pitaevskii equation with NVIDIA GPU acceleration.

A python package simulating the quasi-2D pseudospin-1/2 Gross-Pitaevskii equation with NVIDIA GPU acceleration. Introduction spinor-gpe is high-level,

2 Sep 20, 2022
Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

Optimization as a Model for Few-Shot Learning This repo provides a Pytorch implementation for the Optimization as a Model for Few-Shot Learning paper.

Albert Berenguel Centeno 238 Jan 04, 2023
The code for our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention.

CrossFormer This repository is the code for our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention. Introduction Existin

cheerss 238 Jan 06, 2023
Auto HMM: Automatic Discrete and Continous HMM including Model selection

Auto HMM: Automatic Discrete and Continous HMM including Model selection

Chess_champion 29 Dec 07, 2022
Implementation of "Selection via Proxy: Efficient Data Selection for Deep Learning" from ICLR 2020.

Selection via Proxy: Efficient Data Selection for Deep Learning This repository contains a refactored implementation of "Selection via Proxy: Efficien

Stanford Future Data Systems 70 Nov 16, 2022
Yolov5 + Deep Sort with PyTorch

딥소트 수정중 Yolov5 + Deep Sort with PyTorch Introduction This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of obj

1 Nov 26, 2021
GAN-based Matrix Factorization for Recommender Systems

GAN-based Matrix Factorization for Recommender Systems This repository contains the datasets' splits, the source code of the experiments and their res

Ervin Dervishaj 9 Nov 06, 2022
An implementation of "Learning human behaviors from motion capture by adversarial imitation"

Merel-MoCap-GAIL An implementation of Merel et al.'s paper on generative adversarial imitation learning (GAIL) using motion capture (MoCap) data: Lear

Yu-Wei Chao 34 Nov 12, 2022
Pre-trained NFNets with 99% of the accuracy of the official paper

NFNet Pytorch Implementation This repo contains pretrained NFNet models F0-F6 with high ImageNet accuracy from the paper High-Performance Large-Scale

Benjamin Schmidt 133 Dec 09, 2022
PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

Deepvoice3_pytorch PyTorch implementation of convolutional networks-based text-to-speech synthesis models: arXiv:1710.07654: Deep Voice 3: Scaling Tex

Ryuichi Yamamoto 1.8k Jan 08, 2023
Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Image Crop Analysis This is a repo for the code used for reproducing our Image Crop Analysis paper as shared on our blog post. If you plan to use this

Twitter Research 239 Jan 02, 2023