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4DVLT: Dynamic Scene Understanding with Worldline-Centered Vision-Language Tracking

2026-10-14 08:38:19
Boxue Yang, Chaoyue Li, Shengyao Zhou, Rui Qian, Haoyang Wu, Shaoqiu Zhang, Linfeng Zhang
arXiv_CV
             
Abstract

Language-guided dynamic scene understanding requires identifying one physical entity across time, metric 3D space, and multiple viewpoints, a setting not captured by current multimodal reasoning or tracking paradigms. We introduce 4DVLT, an offline calibrated multi-view task that unifies grounding with metric 3D and synchronized 2D tracking through a worldline binding identity, motion, visibility, and observations. We construct Instruct-4D, with 129.4K QA pairs, 64.7K targets, 851 scenes, and 9 reasoning types, and propose 4DTrack for query-conditioned inference over a 4D state graph. 4DTrack reaches 55.18 WQS, exceeding the strongest adapted VLT baseline by 33.01 points while improving grounding and trajectory quality. The project page is available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2606.22631

PDF

https://arxiv.org/pdf/2606.22631.pdf

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