💡 The paper introduces PhysX-Omni, a unified framework for generating simulation-ready 3D assets with physical properties across multiple categories. The problem addressed is that existing 3D generation methods either neglect physical properties or are limited to a single asset category, such as rigid, deformable, or articulated objects. To address this, the authors develop a novel geometry representation tailored for vision-language models, which directly encodes high-resolution 3D structures without compression, significantly improving generation performance.

The PhysX-Omni framework generates simulation-ready physical 3D assets using this novel geometry representation. The authors also construct the first general simulation-ready 3D dataset, PhysXVerse, covering diverse indoor and outdoor categories. To evaluate the framework, they propose PhysX-Bench, a benchmark that encompasses six key attributes: geometry, absolute scale, material, affordance, kinematics, and function description.

The results show that PhysX-Omni performs strongly in both generation and understanding, outperforming conventional metrics and PhysX-Bench. Additional studies validate the potential of PhysX-Omni for applications in simulation-ready scene generation and robotic policy learning. The authors believe that PhysX-Omni can significantly advance a wide range of downstream applications, particularly in embodied AI and physics-based simulation.

The key contributions of the paper are the development of a novel geometry representation, the construction of the PhysXVerse dataset, and the proposal of the PhysX-Bench benchmark. These contributions enable the generation of simulation-ready physical 3D assets across multiple categories, which can be used in various applications such as robotics, computer vision, and simulation. Overall, the paper presents a significant advancement in the field of 3D generation and simulation, with potential applications in a wide range of areas.