Tracking Anything in High Quality
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A framework for high-quality tracking-anything in videos combining segmentation and refinement.
Two-stage design: Combines a video multi-object segmenter with a pretrained mask refiner model to clean up tracking output.
Mask quality focus: Addresses the common failure mode where trackers lose object boundaries over time, maintaining sharp masks across long clips.
VOTS2023 results: Ranked 2nd place in the VOTS2023 challenge, demonstrating competitive quality against specialized trackers.
Practical tool: Useful for video editing, AR/VR, and content creation pipelines that require pixel-accurate object tracking over long sequences.
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