MatPhys: Learning Material-Aware Physics Parameters for Deformable Object Simulation from Videos

TL;DR

MatPhys is a novel framework that predicts material-specific physics parameters from videos, enabling consistent deformable object simulation across different scenes and interactions.

Contribution

It introduces a part-level material decomposition and a shared material codebook to improve generalization and consistency in physics parameter estimation from monocular videos.

Findings

Matches per-scene optimization in reconstruction and prediction

Achieves stronger generalization to unseen interactions

Produces more consistent physical parameters across scenes

Abstract

Reconstructing simulation-ready deformable objects is important for vision, graphics, and robotics. Existing physics-driven methods can recover physical digital twins from videos, but they suffer from two fundamental limitations: they typically assume a homogeneous material across the whole object, and their scene-specific inverse optimization, combined with the inherent ambiguity of monocular observation, yields inconsistent parameters for the same material across different scenes or interactions. We propose MatPhys, a material-aware feed-forward framework that predicts spring-mass parameters from a single-view video, addressing these two issues with two coupled designs. To relax the homogeneous material assumption, we use DINO features to decompose the object into semantically meaningful parts and to query a part-level material prior, assigning each part its own physical behavior. To enforce cross-scene consistency, we introduce a learned material codebook of shared material embeddings as the bridge between appearance and physics, and further use the part-level prior as a reference distribution that constrains the decoder so that the same material yields consistent parameters across scenes and interactions. Together, these designs turn an under-constrained monocular problem into feed-forward inference grounded on shared, reusable material concepts. Experiments show that our method matches per-scene optimization baselines in reconstruction and future prediction, while achieving stronger generalization to unseen interactions and objects with more consistent physical parameters.