3.4k citations · 3.5k across the 19 of their papers we have counts for
26 papers · 1 filter
TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views
Skanda Koppula, Frano Rajic, Abdullah Faiz Ur Rahman +9
Multi-camera systems are increasingly practical for robotics, AR/VR, and autonomous driving because complementary views reduce depth ambiguity and preserve visibility under occlusi…
TAPNext++: What's Next for Tracking Any Point (TAP)?
Sebastian Jung, Artem Zholus, Martin Sundermeyer +6
Tracking-Any-Point (TAP) models aim to track any point through a video which is a crucial task in AR/XR and robotics applications. The recently introduced TAPNext approach proposes…
Forecasting Motion in the Wild
Neerja Thakkar, Shiry Ginosar, Jacob Walker +3
Visual intelligence requires anticipating the future behavior of agents, yet vision systems lack a general representation for motion and behavior. We propose dense point trajectori…
Frozen Forecasting: A Unified Evaluation
Jacob C Walker, Pedro Vélez, Luisa Polania Cabrera +7
Forecasting future events is a fundamental capability for general-purpose systems that plan or act across different levels of abstraction. Yet, evaluating whether a forecast is "co…
Direct Motion Models for Assessing Generated Videos
Kelsey Allen, Carl Doersch, Guangyao Zhou +9
A current limitation of video generative video models is that they generate plausible looking frames, but poor motion -- an issue that is not well captured by FVD and other popular…
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus, Carl Doersch, Yi Yang +7
Tracking Any Point (TAP) in a video is a challenging computer vision problem with many demonstrated applications in robotics, video editing, and 3D reconstruction. Existing methods…