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/

翻译：现有前馈网络擅长从视觉外观预测单一物理属性集，但这种点估计范式从根本上无法捕捉真实世界固有的物理模糊性。我们通过将物理预测重构为学习材料属性的可控连续分布任务来解决这一问题。我们提出UNIPIXIE框架，该框架经过训练，能从单一视觉输入预测物理合理材料属性的连续参数化路径。通过在我们构建的PIXIEMULTIVERSE数据集上学习物体从最柔到最硬谱系的直接映射，UNIPIXIE允许通过单个直观参数可控生成多样且物理有效的材料场。关键在于，UNIPIXIE引入了一种新颖的统一架构，用于为多种物理求解器生成可直接用于仿真的参数，包括基于连续介质的物质点法（MPM）、基于线性混合蒙皮（LBS）的降阶变形以及基于锚点的弹簧-质量系统，解决了先前工作中的关键可移植性问题。实验表明，我们的方法不仅能生成丰富多样的合理动力学行为，还将杨氏模量预测误差相较于最强确定性基线降低了50%以上，弥合了静态点估计与物理现实连续本质之间的鸿沟。项目页面：https://unipixie.github.io/