无需预设材质就能从视频中识别物体物理属性和几何结构
MASIV: Toward Material-Agnostic System Identification from Videos
- 用可学习的神经本构模型替代手工设定的物理定律
- 在无完整粒子状态条件下仍实现高精度几何与渲染效果
- 适合需要泛化能力的物理模拟与视频重建场景
从视频中进行系统识别旨在恢复物体几何形状和支配的物理规律。现有方法结合可微渲染与仿真，但依赖预设的材质先验，难以处理未知材质。我们提出MASIV，首个基于视觉的材质无关系统识别框架。不同于依赖手工设计本构定律的方法，MASIV采用可学习的神经本构模型，在不假设特定场景材质先验的前提下推断物体动态。然而，缺乏完整的粒子状态信息带来了独特挑战，导致优化不稳定和物理上不合理的现象。为此，我们通过重建连续介质粒子轨迹引入密集几何引导，提供超越稀疏视觉线索的时序运动约束。全面实验表明，MASIV在几何精度、渲染质量与泛化能力上均达到当前最优水平。
原文摘要 · Abstract (English)
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 trajectories, 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.
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