Person re-identification (ReID) across aerial and ground views at extreme far distances introduces a distinct operating regime where severe resolution degradation, extreme viewpoint changes, unstable motion cues, and clothing variation jointly undermine the appearance-based assumptions of existing ReID systems. To study this regime, we introduce VReID-XFD, a video-based benchmark and community challenge for extreme far-distance (XFD) aerial-to-ground person re-identification. VReID-XFD is derived from the DetReIDX dataset and comprises 371 identities, 11,288 tracklets, and 11.75 million frames, captured across altitudes from 5.8 m to 120 m, viewing angles from oblique (30 degrees) to nadir (90 degrees), and horizontal distances up to 120 m. The benchmark supports aerial-to-aerial, aerial-to-ground, and ground-to-aerial evaluation under strict identity-disjoint splits, with rich physical metadata. The VReID-XFD-25 Challenge attracted 10 teams with hundreds of submissions. Systematic analysis reveals monotonic performance degradation with altitude and distance, a universal disadvantage of nadir views, and a trade-off between peak performance and robustness. Even the best-performing SAS-PReID method achieves only 43.93 percent mAP in the aerial-to-ground setting. The dataset, annotations, and official evaluation protocols are publicly available at https://www.it.ubi.pt/DetReIDX/ .
@article{arxiv.2601.01312,
title = {VReID-XFD: Video-based Person Re-identification at Extreme Far Distance Challenge Results},
author = {Kailash A. Hambarde and Hugo Proença and Md Rashidunnabi and Pranita Samale and Qiwei Yang and Pingping Zhang and Zijing Gong and Yuhao Wang and Xi Zhang and Ruoshui Qu and Qiaoyun He and Yuhang Zhang and Thi Ngoc Ha Nguyen and Tien-Dung Mai and Cheng-Jun Kang and Yu-Fan Lin and Jin-Hui Jiang and Chih-Chung Hsu and Tamás Endrei and György Cserey and Ashwat Rajbhandari},
journal= {arXiv preprint arXiv:2601.01312},
year = {2026}
}