ArtGen: Conditional Generative Modeling of Articulated Objects in Arbitrary Part-Level States

• URL: http://arxiv.org/abs/2512.12395v1
• Date: Sat, 13 Dec 2025 17:00:03 GMT
• Title: ArtGen: Conditional Generative Modeling of Articulated Objects in Arbitrary Part-Level States
• Authors: Haowen Wang, Xiaoping Yuan, Fugang Zhang, Rui Jian, Yuanwei Zhu, Xiuquan Qiao, Yakun Huang,
• Abstract summary: ArtGen is a conditional diffusion-based framework capable of generating articulated 3D objects with accurate geometry and coherent kinematics.<n>Specifically, ArtGen employs cross-state Monte Carlo sampling to explicitly enforce global kinematic consistency.<n>A compositional 3D-VAE latent prior enhanced with local-global attention effectively captures fine-grained geometry and global part-level relationships.
• Score: 9.721009445297716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating articulated assets is crucial for robotics, digital twins, and embodied intelligence. Existing generative models often rely on single-view inputs representing closed states, resulting in ambiguous or unrealistic kinematic structures due to the entanglement between geometric shape and joint dynamics. To address these challenges, we introduce ArtGen, a conditional diffusion-based framework capable of generating articulated 3D objects with accurate geometry and coherent kinematics from single-view images or text descriptions at arbitrary part-level states. Specifically, ArtGen employs cross-state Monte Carlo sampling to explicitly enforce global kinematic consistency, reducing structural-motion entanglement. Additionally, we integrate a Chain-of-Thought reasoning module to infer robust structural priors, such as part semantics, joint types, and connectivity, guiding a sparse-expert Diffusion Transformer to specialize in diverse kinematic interactions. Furthermore, a compositional 3D-VAE latent prior enhanced with local-global attention effectively captures fine-grained geometry and global part-level relationships. Extensive experiments on the PartNet-Mobility benchmark demonstrate that ArtGen significantly outperforms state-of-the-art methods.

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