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arXiv · 2603.06022

MOSIV: Multi-Object System Identification from Videos

Chunjiang Liu·Xiaoyuan Wang·Qingran Lin·Albert Xiao·Haoyu Chen·Shizheng Wen·Hao Zhang·Lu Qi·Ming-Hsuan Yang·Laszlo A. Jeni·Min Xu·Yizhou Zhao
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Abstract

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.

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Chunjiang Liu, Xiaoyuan Wang, Qingran Lin, Albert Xiao, Haoyu Chen, Shizheng Wen, Hao Zhang, Lu Qi, Ming-Hsuan Yang, Laszlo A. Jeni, Min Xu, Yizhou Zhao. 2026-03-06. MOSIV: Multi-Object System Identification from Videos. https://arxiv.org/abs/2603.06022

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