PAct: Part-Decomposed Single-View Articulated Object Generation
Qingming Liu, Xinyue Yao, Shuyuan Zhang, Yueci Deng, Guiliang Liu, Zhen Liu, Kui Jia

TL;DR
This paper presents PAct, a fast, part-centric generative model that creates articulated 3D objects from a single image, ensuring accurate part structure, motion, and instance consistency without lengthy optimization.
Contribution
The paper introduces a novel part-aware generative framework for articulated object creation that enables fast, instance-consistent 3D asset synthesis from a single view.
Findings
Outperforms optimization-based methods in accuracy and speed
Produces more consistent and plausible articulated structures
Significantly reduces inference time compared to baselines
Abstract
Articulated objects are central to interactive 3D applications, including embodied AI, robotics, and VR/AR, where functional part decomposition and kinematic motion are essential. Yet producing high-fidelity articulated assets remains difficult to scale because it requires reliable part decomposition and kinematic rigging. Existing approaches largely fall into two paradigms: optimization-based reconstruction or distillation, which can be accurate but often takes tens of minutes to hours per instance, and inference-time methods that rely on template or part retrieval, producing plausible results that may not match the specific structure and appearance in the input observation. We introduce a part-centric generative framework for articulated object creation that synthesizes part geometry, composition, and articulation under explicit part-aware conditioning. Our representation models an…
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Taxonomy
Topics3D Shape Modeling and Analysis · Robot Manipulation and Learning · Human Motion and Animation
