# Synthetic-To-Real Video Person Re-ID

arXiv: https://arxiv.org/abs/2402.02108 · HTML: https://mlsanity.com/paper/2402.02108/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [Beyond Discrete Samples: High Information Density Replay for Efficient Lifelong Person Re-Identification](https://mlsanity.com/paper/2508.01587/) (2025)
- [CD-RMOT-Bench: Benchmarking the Cross-Domain Referring Multi-Object Tracking](https://mlsanity.com/paper/2607.25239/) (2026)

## Abstract (arXiv)

Person re-identification (Re-ID) is an important task and has significant applications for public security and information forensics, which has progressed rapidly with the development of deep learning. In this work, we investigate a novel and challenging setting of Re-ID, i.e., cross-domain video-based person Re-ID. Specifically, we utilize synthetic video datasets as the source domain for training and real-world videos for testing, notably reducing the reliance on expensive real data acquisition and annotation. To harness the potential of synthetic data, we first propose a self-supervised domain-invariant feature learning strategy for both static and dynamic (temporal) features. Additionally, to enhance person identification accuracy in the target domain, we propose a mean-teacher scheme incorporating a self-supervised ID consistency loss. Experimental results across five real datasets validate the rationale behind cross-synthetic-real domain adaptation and demonstrate the efficacy of our method. Notably, the discovery that synthetic data outperforms real data in the cross-domain scenario is a surprising outcome. The code and data are publicly available at https://github.com/XiangqunZhang/UDA_Video_ReID.