PhysX-Omni：面向刚体、可变形体与关节物体的统一仿真就绪物理3D生成

摘要

可模拟的物理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生成方法要么忽略物理属性，要么局限于单一资产类别（如刚体、可变形或铰接物体）。为解决这些局限，我们提出PhysX-Omni——一个面向多样化资产类型的统一可模拟物理3D生成框架。具体而言，我们为视觉语言模型开发了一种新颖且高效的几何表征方法，该方法可直接编码高分辨率3D结构而无需压缩，显著提升了生成性能。此外，我们构建了首个通用可模拟3D数据集PhysXVerse，涵盖多样化的室内外类别。同时，为全面且灵活地评估生成与理解能力，我们提出PhysX-Bench，其包含六大关键属性：几何、绝对尺度、材质、可操作功能、运动学及功能描述。基于传统指标与PhysX-Bench的广泛实验表明，PhysX-Omni在生成与理解任务中均表现优异。进一步研究还验证了PhysX-Omni在可模拟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将显著推动下游应用的发展，尤其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.