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.

翻译：我们提出ARTrackV2，它整合了跟踪的两个关键方面：确定目标对象在视频帧中的位置（定位）以及如何描述其外观（外观分析）。在先前版本的基础上，ARTrackV2通过引入一个统一的生成式框架，以自回归方式“读出”目标的轨迹并“复述”其外观，从而扩展了这一概念。该方法实现了一种时间连续性的建模方式，在先前估计的引导下，联合演化运动与视觉特征。此外，ARTrackV2以其高效性和简洁性脱颖而出，避免了低效的帧内自回归和用于外观更新的手动调整参数。尽管设计简洁，ARTrackV2在主流基准数据集上实现了最先进的性能，并展现出显著的效率提升。具体而言，ARTrackV2在GOT-10k上取得79.5%的AO分数，在TrackingNet上取得86.1%的AUC，同时速度比ARTrack快3.6倍。代码将开源。