[Submitted on 3 Sep 2026]
Title:Object Concepts Emerge from Motion
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Abstract:Object-centric visual representations are important for physical-world perception, but existing visual pretraining methods often capture semantic categories without preserving the identity and coherence of individual instances. We present a biologically inspired framework that learns object-centric representations for single images from raw videos. Our approach uses motion boundaries as a source of object-level grouping: off-the-shelf optical flow and clustering produce pseudo-instance masks, which supervise a single-image encoder with pixel-level pairwise metric learning. The framework requires neither human annotations nor camera calibration. We first obtain 195 million pseudo-labeled frames from 7,163 hours of driving and web videos, then expand the supervision to 421 million frames with Motion-Verified Self-Training, which combines model proposals with motion evidence. We train encoders up to Swin-H and distill the learned representations into a family of Swin backbones. Across monocular depth estimation, 3D object detection, 3D occupancy prediction, and end-to-end planning, the resulting models achieve competitive or superior performance relative to supervised and self-supervised pretraining baselines, with particularly strong transfer on geometry- and instance-sensitive tasks. These results show that motion-derived supervision can teach static image encoders to represent visual instances, providing a complementary direction for scalable visual pretraining.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.04348 [cs.CV]
(or arXiv:2609.04348v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.04348
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Boshi Li [view email] [v1] Thu, 3 Sep 2026 18:11:53 UTC (9,972 KB)
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