Toward Material-Agnostic System Identification From Videos
Zhao, Yizhou Chen, Haoyu Liu, Chunjiang Li, Zhenyang Herrmann, Charles Hur, Junhwa Li, Yinxiao Yang, Ming-Hsuan Raj, Bhiksha Xu, Min
Zhao, Yizhou
Chen, Haoyu
Liu, Chunjiang
Li, Zhenyang
Herrmann, Charles
Hur, Junhwa
Li, Yinxiao
Yang, Ming-Hsuan
Raj, Bhiksha
Xu, Min
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Department
Computer Vision
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Conference proceeding
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Abstract
System identification from videos aims to recover object geometry and governing physical laws. Existing methods integrate differentiable rendering with simulation but rely on predefined material priors, limiting their ability to handle unknown ones. We introduce MASIV, the first vision-based framework for material-agnostic system identification. Unlike existing approaches that depend on hand-crafted constitutive laws, MASIV employs learnable neural constitutive models, inferring object dynamics without assuming a scene-specific material prior. However, the absence of full particle state information imposes unique challenges, leading to unstable optimization and physically implausible behaviors. To address this, we introduce dense geometric guidance by reconstructing continuum particle trajecto-ries, providing temporally rich motion constraints beyond sparse visual cues. Comprehensive experiments show that MASIV achieves state-of-the-art performance in geometric accuracy, rendering quality, and generalization ability.
Citation
Y. Zhao, H. Chen, C. Liu, Z. Li, C. Herrmann, J. Hur , et al., "Toward Material-Agnostic System Identification From Videos," 2026, pp. 5944-5956.
Source
2025 IEEE/CVF International Conference on Computer Vision (ICCV)
Conference
2025 IEEE/CVF International Conference on Computer Vision (ICCV)
Keywords
46 Information and Computing Sciences, 4607 Graphics, Augmented Reality and Games
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Source
2025 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
IEEE