Point Prompting: Counterfactual Tracking with Video Diffusion Models
Authors: name: Ayush Shrivastava, name: Sanyam Mehta, name: Daniel Geng, name: Andrew Owens

Trackers and video generators solve closely related problems: the former
analyze motion, while the latter synthesize it. We show that this connection
enables pretrained video diffusion models to perform zero-shot point tracking
by simply prompting them to visually mark points as they move over time. We
place a distinctively colored marker at the query point, then regenerate the
rest of the video from an intermediate noise level. This propagates the marker
across frames, tracing the point's trajectory. To ensure that the marker
remains visible in this counterfactual generation, despite such markers being
unlikely in natural videos, we use the unedited initial frame as a negative
prompt. Through experiments with multiple image-conditioned video diffusion
models, we find that these "emergent" tracks outperform those of prior
zero-shot methods and persist through occlusions, often obtaining performance
that is competitive with specialized self-supervised models.