We present ARTrackV2, which integrates two pivotal aspects of tracking:
determining where to look (localization) and how to describe (appearance
analysis) the target object across video frames. Building on the foundation of
its predecessor, ARTrackV2 extends the concept by introducing a unified
generative framework to "read out" object's trajectory and "retell" its
appearance in an autoregressive manner. This approach fosters a time-continuous
methodology that models the joint evolution of motion and visual features,
guided by previous estimates. Furthermore, ARTrackV2 stands out for its
efficiency and simplicity, obviating the less efficient intra-frame
autoregression and hand-tuned parameters for appearance updates. Despite its
simplicity, ARTrackV2 achieves state-of-the-art performance on prevailing
benchmark datasets while demonstrating remarkable efficiency improvement. In
particular, ARTrackV2 achieves AO score of 79.5\% on GOT-10k, and AUC of 86.1\%
on TrackingNet while being $3.6 \times$ faster than ARTrack. The code will be
released.

ARTrackV2 是一种集成了定位和外观分析两个关键方面的追踪方法，通过引入统一生成框架，以自回归方式 “阅读” 目标物体的轨迹和 “复述” 其外观，实现对运动和视觉特征的时间连续建模，从而提高了精确性和效率。