| title | ReferTrack: Referring Then Tracking for Embodied Visual Tracking |
|---|---|
| authors | Hanjing Ye, Tianle Zeng, Jiazhao Zhang, Shaoan Wang, Zibo Zhang, Weisi Situ, Yuchen Zhou, Yonggen Ling, Hong Zhang |
| date | 2026-07-22 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.20061 |
| affiliation | Tencent |
| upvotes | 46 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Embodied visual tracking (EVT) requires a mobile agent to continuously follow a specific target described in natural language using only onboard vision. While recent vision-language-action (VLA) policies unify target identification and trajectory planning, their chain-of-thought (CoT) reasoning often operates in abstract spatial latents that are difficult to supervise and weakly aligned with explicit image-space detections. To address this, we introduce ReferTrack, a referring-then-tracking paradigm that grounds EVT using a single forward-facing camera. Our model first selects the target from an indexed set of bounding boxes, then decodes tracking waypoints conditioned on this image-grounded decision. To preserve target motion cues over time, ReferTrack maintains a sliding-window queue of previously selected bounding boxes, injecting their geometric features into the visual history via temporal-viewpoint-bbox indicator (TVBI) tokens. We further enhance target identification by co-training on a custom Refer-QA dataset. On EVT-Bench, ReferTrack achieves state-of-the-art single-view performance with success rates of 89.4%, 73.3%, and 74.1% on the single-target, distracted, and ambiguity tracking splits, respectively -- matching or even surpassing several multi-camera baselines on identification-heavy tasks. Finally, real-world deployments on legged and humanoid robots validate its robust sim-to-real transfer capabilities. Code is available at https://github.com/MedlarTea/referTrack. |
cite
@misc{ye2026refertrack,
title={ReferTrack: Referring Then Tracking for Embodied Visual Tracking},
author={Hanjing Ye and Tianle Zeng and Jiazhao Zhang and Shaoan Wang and Zibo Zhang and Weisi Situ and Yuchen Zhou and Yonggen Ling and Hong Zhang},
year={2026},
eprint={2607.20061},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.20061}
}