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This content will become publicly available on June 18, 2024

Title: MOVES: Manipulated Objects in Video Enable Segmentation
Our method uses manipulation in video to learn to understand held-objects and hand-object contact. We train a system that takes a single RGB image and produces a pixel-embedding that can be used to answer grouping questions (do these two pixels go together) as well as hand-association questions (is this hand holding that pixel). Rather than painstakingly annotate segmentation masks, we observe people in realistic video data. We show that pairing epipolar geometry with modern optical flow produces simple and effective pseudo-labels for grouping. Given people segmentations, we can further associate pixels with hands to understand contact. Our system achieves competitive results on hand and hand-held object tasks.  more » « less
Award ID(s):
2006619
NSF-PAR ID:
10469274
Author(s) / Creator(s):
;
Publisher / Repository:
CVPR
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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