This project contains the ClonedPerson dataset and code described in our paper "Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification".

Last update: Dec 27, 2022

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Command-line Tools ClonedPerson

Overview

ClonedPerson

This is the official repository for the ClonedPerson project, which contains the ClonedPerson dataset and code described in our paper "Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification".

Fig. 1. Examples of 3D characters in ClonedPerson.

Dataset Description
Experimental Results
Contacts

Dataset Description

The ClonedPerson dataset is generated by MakeHuman and Unity3D. Characters in this dataset use an automatic approach to directly clone the whole outfits from real-world person images to virtual 3D characters, such that any virtual person thus created will appear very similar to its real-world counterpart. The dataset contains 887,766 synthesized person images of 5,621 identities. 3D characters and their counterparts in this dataset are shown in Fig. 1.

Download Links

ClonedPerson Dataset

Google Drive

Video_first batch

Google Drive

File Structure

clonedperson
├── clonedperson.tar.gz
│   ├── train
│   │      ├── 700000_s02_c00_f023389.jpg
│   │      ├── 700000_s06_c00_f023413.jpg
│   │      ├── ```
│   ├── test
│   │      ├── gallery
│   │      │      ├── 705228_s06_c02_f005521.jpg
│   │      │      ├── 705624_s12_c02_f002965.jpg
│   │      │      ├── ```
│   │      ├── query
│   │      │      ├── 705227_s02_c02_f001117.jpg
│   │      │      ├── 705624_s12_c00_f003229.jpg
│   │      │      ├── ```
├── videos(uploading...)
│   ├── first batch
│   │      ├── Recordings_scene02
│   │      │      ├── person-im
│   │      │      │      ├── pointsCamera0_1.txt  # This file saved the key points of the camera0_1.mp4
│   │      │      │      ├── deal_camera0_1.txt  # This file saved data extracted from the pointsCamera0_1.txt file every few seconds
│   │      │      │      ├── pointsCamera1_1.txt  
│   │      │      │      ├── deal_camera1_1.txt  
│   │      │      │      ├── ```
│   │      ├── camera0_1.mp4  # This is the video corresponding to file images/scene00/camera0_1.tar.gz(or delete_camera0_1.tar.gz)
│   │      ├── camera1_1.mp4
│   │      ├── Recordings_scene03
│   │      │      ├── person-im
│   │      │      │      ├── pointsCamera0_1.txt  # This file saved the key points of the camera0_1.mp4
│   │      │      │      ├── deal_camera0_1.txt  # This file saved data extracted from the pointsCamera0_1.txt file every few seconds
│   │      │      │      ├── pointsCamera1_1.txt  
│   │      │      │      ├── deal_camera1_1.txt  
│   │      │      │      ├── ```
│   │      ├── ```
│   ├── second batch
│   ├── ```

The filenames are encoded as follows. Take "700000_s02_c00_f023389.jpg" as an example,

700000 is the id of the person
s02 is the id of the scene
c00 is the id of the camera
f023389 is the number of frames

Experimental Results

By training person re-identification models on these synthesized person images, we demonstrate that the model trained on ClonedPerson has a better generalization performance, superior to that trained on other popular real-world and synthetic person re-identification datasets.. The experimental results are shown in the following tables.

Contacts

Yanan Wang
Inception Institute of Artificial Intelligence (IIAI)
[email protected]

Citation

@article{Wang-2022-Clonedperson,
  title={{Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification}},
  author={Yanan Wang, Xuezhi Liang and Shengcai Liao},
   journal={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

This project contains the ClonedPerson dataset and code described in our paper "Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification".

Related tags

Overview

ClonedPerson

Table of Contents

Dataset Description

Download Links

File Structure

Experimental Results

Contacts

Citation

Owner

Yanan Wang

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