Tracking and Understanding Object Transformations
Yihong Sun, Xinyu Yang, Jennifer Sun, Bharath Hariharan
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 to object appearance. To address this limitation, we introduce TubeletGraph, a zero-shot system that recovers missing objects after transformation and maps out how the 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 understandings of object transformations and promising capabilities in temporal grounding and semantic reasoning for complex object transformations.