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–Neural Information Processing Systems
Existing 3D generation primarily emphasizes geometries and textures while neglecting physical-grounded modeling. Consequently, despite the rapid development of 3D generative models, the synthesized 3D assets often overlook rich and important physical properties, hampering their real-world application in physical domains like simulation and embodied AI. As an initial attempt to address this challenge, we propose PhysX, an end-to-end paradigm for physical-grounded 3D asset generation. 1) To bridge the critical gap in physics-annotated 3D datasets, we present PhysXNet - the first physics-grounded 3D dataset systematically annotated across five foundational dimensions: absolute scale, material, affordance, kinematics, and function description. In particular, we devise a scalable human-in-the-loop annotation pipeline based on vision-language models, which enables efficient creation of physics-first assets from raw 3D assets.
Neural Information Processing Systems
Jun-19-2026, 09:04:54 GMT
- Country:
- Asia (0.28)
- Genre:
- Research Report > Experimental Study (0.46)
- Technology:
- Information Technology > Artificial Intelligence
- Vision (1.00)
- Natural Language (1.00)
- Machine Learning (1.00)
- Information Technology > Artificial Intelligence