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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2603.06022 |
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| _version_ | 1866910043498086400 |
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| author | Liu, Chunjiang Wang, Xiaoyuan Lin, Qingran Xiao, Albert Chen, Haoyu Wen, Shizheng Zhang, Hao Qi, Lu Yang, Ming-Hsuan Jeni, Laszlo A. Xu, Min Zhao, Yizhou |
| author_facet | Liu, Chunjiang Wang, Xiaoyuan Lin, Qingran Xiao, Albert Chen, Haoyu Wen, Shizheng Zhang, Hao Qi, Lu Yang, Ming-Hsuan Jeni, Laszlo A. Xu, Min Zhao, Yizhou |
| contents | We introduce the challenging problem of multi-object system identification from videos, for which prior methods are ill-suited due to their focus on single-object scenes or discrete material classification with a fixed set of material prototypes. To address this, we propose MOSIV, a new framework that directly optimizes for continuous, per-object material parameters using a differentiable simulator guided by geometric objectives derived from video. We also present a new synthetic benchmark with contact-rich, multi-object interactions to facilitate evaluation. On this benchmark, MOSIV substantially improves grounding accuracy and long-horizon simulation fidelity over adapted baselines, establishing it as a strong baseline for this new task. Our analysis shows that object-level fine-grained supervision and geometry-aligned objectives are critical for stable optimization in these complex, multi-object settings. The source code and dataset will be released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MOSIV: Multi-Object System Identification from Videos Liu, Chunjiang Wang, Xiaoyuan Lin, Qingran Xiao, Albert Chen, Haoyu Wen, Shizheng Zhang, Hao Qi, Lu Yang, Ming-Hsuan Jeni, Laszlo A. Xu, Min Zhao, Yizhou Computer Vision and Pattern Recognition We introduce the challenging problem of multi-object system identification from videos, for which prior methods are ill-suited due to their focus on single-object scenes or discrete material classification with a fixed set of material prototypes. To address this, we propose MOSIV, a new framework that directly optimizes for continuous, per-object material parameters using a differentiable simulator guided by geometric objectives derived from video. We also present a new synthetic benchmark with contact-rich, multi-object interactions to facilitate evaluation. On this benchmark, MOSIV substantially improves grounding accuracy and long-horizon simulation fidelity over adapted baselines, establishing it as a strong baseline for this new task. Our analysis shows that object-level fine-grained supervision and geometry-aligned objectives are critical for stable optimization in these complex, multi-object settings. The source code and dataset will be released. |
| title | MOSIV: Multi-Object System Identification from Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.06022 |