PhysReal: ハイブリッド構成モデリングによる実世界の変形物体物理の学習
PhysReal: Learning Real-World Deformable Object Physics via Hybrid Constitutive Modeling
実世界の変形物体の物理をビデオから学習し、シミュレーションするフレームワークを提案。解析モデルとニューラル残差を組み合わせた構成モデルをMPMシミュレータと3DGSレンダラに統合し、段階的カリキュラムでパラメータを推定する。
著者: Yinan Deng, Jianqiao Song, Yisi Zhang, Yuhan Wang, Jiahui Wang, Yufeng Yue
分類: cs.RO
原文アブストラクト
Learning physically plausible dynamics from visual observations is essential for interactive world models and embodied agents. However, modeling real-world deformable objects remains challenging because their dynamics often arise from complex, spatially heterogeneous material responses. To address this challenge, we propose PhysReal, a video-driven framework for learning and simulating the underlying physics of real deformable objects. PhysReal integrates a spatially varying hybrid expert-neural constitutive model with a differentiable MPM simulator and 3DGS renderer. Analytical expert models provide interpretable physical priors, while neural constitutive residuals capture material responses beyond predefined formulations. Spatially distributed patches parameterize the constitutive field, enabling a continuous representation of local material variations. To organize the identification of this model from sparse visual observations, we adopt a progressive curriculum that sequentially optimizes global material properties, spatially varying local parameters, and neural constitutive residuals, together with complementary motion and mask supervision. Extensive experiments on diverse deformable-object interactions demonstrate that PhysReal achieves superior performance in dynamic reconstruction and future-state prediction, while showing strong potential for downstream robotic applications.