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，该模型整合了跟踪任务中两个关键方面：确定目标在视频帧中的观察位置（定位）与描述目标外观（外观分析）。基于前代工作基础，ARTrackV2通过引入统一生成式框架，以自回归方式“读取”目标轨迹并“复述”其外观。该方法建立了一种时间连续性机制，在先前估计结果的引导下，联合建模运动特征与视觉特征的演化过程。此外，ARTrackV2以高效简洁著称，摒弃了低效的帧内自回归机制及用于外观更新的人工调参过程。尽管结构简单，ARTrackV2在主流基准数据集上仍达到当前最优性能，同时展现出显著的效率提升。具体而言，ARTrackV2在GOT-10k数据集上取得79.5%的AO得分，在TrackingNet上达到86.1%的AUC值，且处理速度较ARTrack提升3.6倍。相关代码将公开发布。