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 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.
69c3567d95b48f16a3007805
Chuanrui Zhang
claimed_verified
2026-03-25T10:23:26.425Z
6530df1de85cff4618e14d27
/avatars/515a85b8e3d1b805619cb0a70f2b3c26.svg
Chuanrui Zhang
xingyoujun
user
xingyoujun
69c3567d95b48f16a3007806
Minghan Qin
claimed_verified
2026-03-25T10:23:24.161Z
642002b51ccd411979d72b18
https://cdn-avatars.huggingface.co/v1/production/uploads/642002b51ccd411979d72b18/JO9e0o8fAFNC-eYFbx_JW.png
Minghan Qin
Qmh
user
Qmh
69c3567d95b48f16a3007807
Yuang Wang
69c3567d95b48f16a3007808
Baifeng Xie
69c3567d95b48f16a3007809
Hang Li
69c3567d95b48f16a300780a
Ziwei Wang
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 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.
A unified multimodal large language model framework called SIMART is proposed for generating articulated 3D assets with reduced tokenization overhead and improved simulation readiness.
articulared object creation MLLMs dense voxel-based 3D tokenization Sparse 3D VQ-VAE part-level decomposition kinematic prediction physics-based robotic simulation