PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects
PhysX-Omni is a unified framework for generating simulation-ready physical 3D assets across diverse categories including rigid, deformable, and articulated objects. It introduces a novel geometry representation tailored for Vision-Language Models that directly encodes high-resolution 3D structures without compression, significantly improving generation performance. The framework also presents the first general simulation-ready 3D dataset, PhysXVerse, and a comprehensive benchmark, PhysX-Bench, for evaluating both generative and understanding capabilities. Experiments demonstrate strong performance in generation and understanding, with potential applications in simulation-ready scene generation and robotic policy learning, advancing embodied AI and physics-based simulation.
[2605.21572] PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects
[Submitted on 20 May 2026]
Title:PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects
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Abstract: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.
Comments: Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
Cite as: arXiv:2605.21572 [cs.CV]
(or arXiv:2605.21572v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.21572
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ziang Cao [view email] [v1] Wed, 20 May 2026 17:59:01 UTC (6,728 KB)
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