mlsanityData to 14 Oct 2026

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

arXiv 2603.23386 ↗First posted 24 Mar 2026cs.CV cs.GR cs.ROcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 53 of its 59 references that are in the map. First posted on arXiv 24 Mar 2026.

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53 of 59 references.

References · 53

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.