  The Segment Anything Model (SAM) has established itself as a powerful
zero-shot image segmentation model, enabled by efficient point-centric
annotation and prompt-based models. While click and brush interactions are both
well explored in interactive image segmentation, the existing methods on videos
focus on mask annotation and propagation. This paper presents SAM-PT, a novel
method for point-centric interactive video segmentation, empowered by SAM and
long-term point tracking. SAM-PT leverages robust and sparse point selection
and propagation techniques for mask generation. Compared to traditional
object-centric mask propagation strategies, we uniquely use point propagation
to exploit local structure information agnostic to object semantics. We
highlight the merits of point-based tracking through direct evaluation on the
zero-shot open-world Unidentified Video Objects (UVO) benchmark. Our
experiments on popular video object segmentation and multi-object segmentation
tracking benchmarks, including DAVIS, YouTube-VOS, and BDD100K, suggest that a
point-based segmentation tracker yields better zero-shot performance and
efficient interactions. We release our code that integrates different point
trackers and video segmentation benchmarks at https://github.com/SysCV/sam-pt.
