SIMART: Decomposing Monolithic Meshes into Sim-ready Articulated Assets via MLLM
This addresses the gap in creating sim-ready interactive objects for embodied AI and physical simulation, representing a novel method rather than an incremental improvement.
The paper tackles the problem of generating articulated 3D assets for simulation by proposing SIMART, a unified MLLM framework that reduces token counts by 70% and achieves state-of-the-art performance on datasets like PartNet-Mobility.
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.