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PhysX-Omni:適用於剛體、可變形體與關節體的統一可模擬物理3D生成

PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects

May 20, 2026
作者: Ziang Cao, Yinghao Liu, Haitian Li, Runmao Yao, Fangzhou Hong, Zhaoxi Chen, Liang Pan, Ziwei Liu
cs.AI

摘要

可直接用于仿真的物理3D资产因其在下游任务中的广泛适用性而成为一个充满前景的研究方向。然而,现有的大多数3D生成方法要么忽略了物理属性,要么局限于单一资产类别(例如刚体、可变形体或铰接物体)。为解决这些局限性,我们提出了PhysX-Omni,一个面向多样化资产类型的统一框架,用于生成可直接用于仿真的物理3D模型。具体而言,我们开发了一种新颖且高效的几何表征方法,专为视觉语言模型设计,能够在不经压缩的情况下直接编码高分辨率3D结构,显著提升了生成性能。此外,我们构建了首个通用的可直接用于仿真的3D数据集PhysXVerse,涵盖多样化的室内外类别。更进一步,为全面且灵活地评估在真实环境中的生成与理解能力,我们提出了PhysX-Bench,包含六个关键属性:几何、绝对尺度、材质、可供性、运动学和功能描述。通过传统评估指标与PhysX-Bench的大量实验表明,PhysX-Omni在生成与理解任务中均表现出色。此外,补充研究进一步验证了PhysX-Omni在可直接用于仿真的场景生成与机器人策略学习等应用中的潜力。我们相信PhysX-Omni将显著推动广泛的下游应用,特别是在具身智能与基于物理的仿真领域。
English
Simulation-ready physical 3D assets have emerged as a promising direction owing to their broad applicability in downstream tasks. However, most existing 3D generation methods either neglect physical properties or are limited to a single asset category, e.g., rigid, deformable, or articulated objects. To address these limitations, we introduce PhysX-Omni, a unified framework for simulation-ready physical 3D generation across diverse asset types. Specifically, we develop a novel and efficient geometry representation tailored for Vision-Language Models, which directly encodes high-resolution 3D structures without compression, significantly improving generation performance. In addition, we construct the first general simulation-ready 3D dataset, PhysXVerse, covering diverse indoor and outdoor categories. Furthermore, to comprehensively and flexibly evaluate both generative and understanding capabilities in the wild, we propose PhysX-Bench, which encompasses six key attributes: geometry, absolute scale, material, affordance, kinematics, and function description. Extensive experiments with conventional metrics and PhysX-Bench show that PhysX-Omni performs strongly in both generation and understanding. Moreover, additional studies further validate the potential of PhysX-Omni for applications in simulation-ready scene generation and robotic policy learning. We believe PhysX-Omni can significantly advance a wide range of downstream applications, particularly in embodied AI and physics-based simulation.