SimArt: Decomposing Monolithic Meshes into Sim-ready Articulated Assets via MLLM

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 performs part-level decomposition and kinematic prediction jointly. 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. Code and data for this paper are at simart-mllm.github.io.