UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching
Quick Answer
UNIPIXIE introduces a novel framework for predicting continuous distributions of material properties from visual inputs, significantly reducing Young's Modulus prediction error by over 50% compared to deterministic models.
Quick Take
UNIPIXIE introduces a novel framework for predicting continuous distributions of material properties from visual inputs, significantly reducing Young's Modulus prediction error by over 50% compared to deterministic models. This unified architecture supports various physics solvers, enhancing portability and enabling controllable generation of diverse material fields.
Key Points
- UNIPIXIE learns a continuous distribution of material properties from a single visual input.
- The framework reduces Young's Modulus prediction error by over 50% against deterministic baselines.
- It supports various physics solvers, including Material Point Method and Spring-Mass systems.
- The approach allows for controllable generation of diverse, physically valid material fields.
- UNIPIXIE addresses key portability issues in prior physics prediction models.
Article Content
From source RSS / original summaryarXiv:2606. 05399v1 Announce Type: new Abstract: Existing feed-forward networks excel at predicting a single set of physical properties from visual appearance, but this point-estimate paradigm fundamentally fails to capture the real world's inherent physical ambiguity. We address this by reframing physics prediction as a task of learning a controllable, continuous distribution of material properties.
We introduce UNIPIXIE, a framework trained to predict a continuous and parameterized path of physically plausible material properties from a single visual input. By learning a direct mapping along an object's softest-to-stiffest spectrum on our PIXIEMULTIVERSE dataset, UNIPIXIE allows for controllable generation of diverse, physically valid material fields via a single intuitive parameter.
Crucially, UNIPIXIE introduces a novel unified architecture to produce simulation-ready parameters for diverse physics solvers, including continuum-based Material Point Method (MPM), reduced-order deformation based on Linear Blend Skinning (LBS), and anchor-based Spring-Mass systems, addressing a key portability issue in prior work.
Experiments show our approach not only generates a rich variety of plausible dynamics but also reduces Young's Modulus prediction error by over 50% against the strongest deterministic baseline, bridging the gap between static point estimates and the continuous nature of physical reality. Project page: https://unipixie. github. io/
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