Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909552304193536 |
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| author | Stojanov, Stefan Wendt, David Kim, Seungwoo Venkatesh, Rahul Feigelis, Kevin Wu, Jiajun Yamins, Daniel LK |
| author_facet | Stojanov, Stefan Wendt, David Kim, Seungwoo Venkatesh, Rahul Feigelis, Kevin Wu, Jiajun Yamins, Daniel LK |
| contents | Estimating motion in videos is an essential computer vision problem with many downstream applications, including controllable video generation and robotics. Current solutions are primarily trained using synthetic data or require tuning of situation-specific heuristics, which inherently limits these models' capabilities in real-world contexts. Despite recent developments in large-scale self-supervised learning from videos, leveraging such representations for motion estimation remains relatively underexplored. In this work, we develop Opt-CWM, a self-supervised technique for flow and occlusion estimation from a pre-trained next-frame prediction model. Opt-CWM works by learning to optimize counterfactual probes that extract motion information from a base video model, avoiding the need for fixed heuristics while training on unrestricted video inputs. We achieve state-of-the-art performance for motion estimation on real-world videos while requiring no labeled data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19953 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals Stojanov, Stefan Wendt, David Kim, Seungwoo Venkatesh, Rahul Feigelis, Kevin Wu, Jiajun Yamins, Daniel LK Computer Vision and Pattern Recognition Estimating motion in videos is an essential computer vision problem with many downstream applications, including controllable video generation and robotics. Current solutions are primarily trained using synthetic data or require tuning of situation-specific heuristics, which inherently limits these models' capabilities in real-world contexts. Despite recent developments in large-scale self-supervised learning from videos, leveraging such representations for motion estimation remains relatively underexplored. In this work, we develop Opt-CWM, a self-supervised technique for flow and occlusion estimation from a pre-trained next-frame prediction model. Opt-CWM works by learning to optimize counterfactual probes that extract motion information from a base video model, avoiding the need for fixed heuristics while training on unrestricted video inputs. We achieve state-of-the-art performance for motion estimation on real-world videos while requiring no labeled data. |
| title | Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.19953 |