论文概要
研究领域: CV 作者: Yunhan Yang, Chunshi Wang, Junliang Ye, Yang Li, Zanxin Chen, Zehuan Huang, Yao Mu, Zhuo Chen, Chunchao Guo, Xihui Liu 发布时间: 2026-05-06 arXiv: 2605.05163中文摘要
合成基于物理的3D资产是交互式虚拟世界和具身AI的关键瓶颈。现有方法主要关注静态几何，忽视了交互所必需的功能属性。我们提出，交互式资产生成必须植根于功能逻辑和层次化物理。为弥合这一差距，我们引入了PhysForge，一个解耦的两阶段框架，由PhysDB支持——一个具有四级物理标注的150,000资产大规模数据集。首先，VLM充当物理架构师来规划定义材料、功能和运动学约束的层次化物理蓝图。其次，基于物理的扩散模型通过新颖的KineVoxel注入（KVI）机制实现这一蓝图，合成高保真几何以及精确的运动学参数。实验表明，PhysForge产生了功能上合理、可立即模拟的资产，为交互式3D内容和具身智能体提供了稳健的数据引擎。原文摘要
Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on static geometry, overlooking the functional properties essential for interaction. We propose that interactive asset generation must be rooted in functional logic and hierarchical physics. To bridge this gap, we introduce PhysForge, a decoupled two-stage framework supported by PhysDB, a large-scale dataset of 150,000 assets with four-tier physical annotations. First, a VLM acts as a physical architect to plan a Hierarchical Physical Blueprint defining material, functional, and kinematic constraints. Second, a physics-grounded diffusion model realizes this blueprint by synthesizing high-fidelity geometry alongside precise kinematic parameters via a novel KineVoxel Injection (KVI) mechanism. Experiments demonstrate that PhysForge produces functionally plausible, simulation-ready assets, providing a robust data引擎 for interactive 3D content and embodied agents.--- *自动采集于 2026-05-08*
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