  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.
