UniPixie：通过流匹配的统一概率三维物理学习
UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching
将物理属性预测从确定性点估计重构为连续分布学习，利用流匹配从单张图像生成可控的材质场，并统一适配 MPM、LBS、弹簧-质点等多种物理求解器，为视觉驱动的物理仿真提供了可复用的概率建模范式。
原文依据 UNIPIXIE introduces a novel unified architecture to produce simulation-ready parameters for diverse physics solvers
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论现有的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新定义为学习可控、连续的材料属性分布来解决这一问题。我们提出 UNIPIXIE，一个训练用于从单一视觉输入预测连续且参数化的物理合理材料属性路径的框架。通过在我们的 PIXIEMULTIVERSE 数据集上学习沿物体最软到最硬光谱的直接映射，UNIPIXIE 允许通过单一直观参数可控地生成多样且物理有效的材料场。关键的是，UNIPIXIE 引入了一种新颖的统一架构，为多种物理求解器（包括基于连续体的物质点法（MPM）、基于线性混合蒙皮的降阶变形（LBS）以及基于锚点的弹簧-质量系统）生成可直接用于仿真的参数，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，而且与最强的确定性基线相比，将杨氏模量预测误差降低了 50% 以上，弥合了静态点估计与物理现实连续性之间的差距。
英文原文摘要 1371 字符
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.