标题： 跟踪和理解物体变换 显示英文标题

标题： Tracking and Understanding Object Transformations

摘要： 现实世界中的物体经常经历状态变换。 从一个苹果被切成块到蝴蝶从茧中孵化出来，跟踪这些变化对于理解现实世界中的物体和动态非常重要。 然而，现有方法在物体发生变换后常常会丢失目标物体，这是由于物体外观发生了显著变化。 为了解决这一限制，我们引入了“Track Any State”任务：在检测和描述状态变化的同时，通过变换跟踪物体，并提供一个新的基准数据集 VOST-TAS。 为了解决这个问题，我们提出了 TubeletGraph，这是一种零样本系统，能够在变换后恢复丢失的物体，并描绘物体状态随时间的变化。 TubeletGraph 首先识别可能被忽略的轨迹，并根据语义和邻近先验判断是否应将其整合。 然后，它对新增的轨迹进行推理，并生成描述每个观察到的变换的状态图。 TubeletGraph 在变换下实现了最先进的跟踪性能，同时展示了对物体变换更深入的理解，并在复杂物体变换的时间定位和语义推理方面表现出色。 代码、附加结果和基准数据集可在 https://tubelet-graph.github.io 获取。 显示英文摘要

摘要： Real-world objects frequently undergo state transformations. From an apple being cut into pieces to a butterfly emerging from its cocoon, tracking through these changes is important for understanding real-world objects and dynamics. However, existing methods often lose track of the target object after transformation, due to significant changes in object appearance. To address this limitation, we introduce the task of Track Any State: tracking objects through transformations while detecting and describing state changes, accompanied by a new benchmark dataset, VOST-TAS. To tackle this problem, we present TubeletGraph, a zero-shot system that recovers missing objects after transformation and maps out how object states are evolving over time. TubeletGraph first identifies potentially overlooked tracks, and determines whether they should be integrated based on semantic and proximity priors. Then, it reasons about the added tracks and generates a state graph describing each observed transformation. TubeletGraph achieves state-of-the-art tracking performance under transformations, while demonstrating deeper understanding of object transformations and promising capabilities in temporal grounding and semantic reasoning for complex object transformations. Code, additional results, and the benchmark dataset are available at https://tubelet-graph.github.io.