SIMART: 通过MLLM将单体网格分解为可模拟的关节化资产
SIMART: Decomposing Monolithic Meshes into Sim-ready Articulated Assets via MLLM
AI总结：
SIMART通过统一的MLLM框架实现部分级分解和运动学预测，减少3D令牌数量，提升多部分组装的保真度，并在PartNet-Mobility和野外AIGC数据集上取得最佳性能，支持物理仿真的机器人模拟。
摘要：
高质量的关节化3D资产对具身AI和物理模拟至关重要，但3D生成仍聚焦于静态网格，缺乏
英文摘要：
High-quality articulated 3D assets are indispensable for embodied AI and physical simulation, yet 3D generation still focuses on static meshes, leaving a gap in "sim-ready" interactive objects. Most recent articulated object creation methods rely on multi-stage pipelines that accumulate errors across decoupled modules. Alternatively, unified MLLMs offer a single-stage path to joint static asset understanding and sim-ready asset generation. However dense voxel-based 3D tokenization yields long 3D token sequences and high memory overhead, limiting scalability to complex articulated objects. To address this, we propose SIMART, a unified MLLM framework that jointly performs part-level decomposition and kinematic prediction. By introducing a Sparse 3D VQ-VAE, SIMART reduces token counts by 70% vs. dense voxel tokens, enabling high-fidelity multi-part assemblies. SIMART achieves state-of-the-art performance on PartNet-Mobility and in-the-wild AIGC datasets, and enables physics-based robotic simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
