From Synthetic to Real: Unveiling the Power of Synthetic Data for Video Person Re-ID

• URL: http://arxiv.org/abs/2402.02108v1
• Date: Sat, 3 Feb 2024 10:19:21 GMT
• Title: From Synthetic to Real: Unveiling the Power of Synthetic Data for Video Person Re-ID
• Authors: Xiangqun Zhang, Ruize Han, Wei Feng
• Abstract summary: We study a new problem of cross-domain video based person re-identification (Re-ID) We take the synthetic video dataset as the source domain for training and use the real-world videos for testing. We are surprised to find that the synthetic data performs even better than the real data in the cross-domain setting.
• Score: 15.81210364737776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study a new problem of cross-domain video based person re-identification (Re-ID). Specifically, we take the synthetic video dataset as the source domain for training and use the real-world videos for testing, which significantly reduces the dependence on real training data collection and annotation. To unveil the power of synthetic data for video person Re-ID, we first propose a self-supervised domain invariant feature learning strategy for both static and temporal features. Then, to further improve the person identification ability in the target domain, we develop a mean-teacher scheme with the self-supervised ID consistency loss. Experimental results on four real datasets verify the rationality of cross-synthetic-real domain adaption and the effectiveness of our method. We are also surprised to find that the synthetic data performs even better than the real data in the cross-domain setting.

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