An adaptive pipeline for costless person re-identification

Released in: UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification



The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. UnrealPerson is a novel pipeline that makes full use of unreal image data to decrease the costs in both the training and deployment stages. Its fundamental part is a system that can generate synthesized images of high-quality and from controllable distributions. Instance-level annotation goes with the synthesized data and is almost free. We point out some details in image synthesis that largely impact the data quality. With 3,000 IDs and 120,000 instances, this method achieves a 38.5% rank-1 accuracy when being directly transferred to MSMT17. It almost doubles the former record using synthesized data and even surpasses previous direct transfer records using real data. This offers a good basis for unsupervised domain adaption, where our pre-trained model is easily plugged into the state-of-the-art algorithms towards higher accuracy. In addition, the data distribution can be flexibly adjusted to fit some corner ReID scenarios, which widens the application of our pipeline.


Images in dataset


Year Released

Key Links & Stats


@inproceedings{zhang2021unrealperson, title={UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification}, author={Tianyu Zhang and Lingxi Xie and Longhui Wei and Zijie Zhuang and Yongfei Zhang and Bo Li and Qi Tian}, year={2021}, booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)} }



  1. Still Image


  1. Digital Human

ML Task

  1. Object Detection
  2. Semantic Segmentation
  3. Instance Segmentation
  4. Object Recognition
  5. Object Tracking

Related organizations

Beihang University

Tsinghua University