SIMART: MLLMによるモノリシックメッシュのシミュレーション対応関節アセットへの分解
SIMART: Decomposing Monolithic Meshes into Sim-ready Articulated Assets via MLLM
静的メッシュを、物理シミュレーションに使える関節付き3Dアセットへ変換する統一MLLMフレームワークを提案。スパース3D VQ-VAEでトークン数を削減し、部品分解と関節予測を同時に行う。
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著者: Chuanrui Zhang, Minghan Qin, Yuang Wang, Baifeng Xie, Hang Li, Ziwei Wang
分類: cs.CV, cs.GR, cs.RO
原文アブストラクト
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.
