PhysX-3D：基于物理的3D资产生成
PhysX-3D: Physical-Grounded 3D Asset Generation
AI总结：
PhysX-3D提出一种基于物理的3D资产生成方法，通过构建物理标注数据集和双分支框架，提升生成资产的物理合理性和几何质量。
摘要：
3D建模正从虚拟走向物理。现有的3D生成主要强调几何和纹理，而忽略了基于物理的建模。因此，尽管3D生成模型迅速发展，合成的3D资产往往忽视了丰富而重要的物理属性，阻碍了其在物理领域如模拟和具身AI中的实际应用。作为初步尝试，我们提出了PhysX-3D，一种端到端的基于物理的3D资产生成范式。1）为填补物理标注3D数据集中的关键缺口，我们提出了PhysXNet——首个系统标注的3D数据集，覆盖五个基础维度：绝对尺度、材料、可操作性、运动学和功能描述。特别是，我们设计了一种基于视觉语言模型的可扩展的人工参与标注流程，能够高效地从原始3D资产中生成以物理优先的资产。2）此外，我们提出了PhysXGen，一种用于基于物理的图像到3D资产生成的前馈框架，将物理知识注入预训练的3D结构空间。具体而言，PhysXGen采用双分支架构，显式建模3D结构与物理属性之间的潜在相关性，从而生成具有合理物理预测的3D资产，同时保持原生几何质量。大量实验验证了该框架的优越性能和良好的泛化能力。所有代码、数据和模型将被发布，以促进生成物理AI的未来研究。
英文摘要：
3D modeling is moving from virtual to physical. 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 \textbf{PhysX-3D}, 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.2) Furthermore, we propose \textbf{PhysXGen}, a feed-forward framework for physics-grounded image-to-3D asset generation, injecting physical knowledge into the pre-trained 3D structural space. Specifically, PhysXGen employs a dual-branch architecture to explicitly model the latent correlations between 3D structures and physical properties, thereby producing 3D assets with plausible physical predictions while preserving the native geometry quality. Extensive experiments validate the superior performance and promising generalization capability of our framework. All the code, data, and models will be released to facilitate future research in generative physical AI.
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