Articulate Anything: Open-vocabulary 3D Articulated Object Generation

ICLR 2025 Conference Submission951 Authors

Keywords: 3D articulated objects, visual prompting, URDF prediction

Abstract:

3D articulated objects modeling has long been a challenging problem, since it requires to capture both accurate surface geometries and semantically meaningful and spatially precise structures, parts, and joints. Existing methods heavily depend on training data from a limited set of handcrafted articulated object categories (\textit{e.g.}, cabinets and drawers), which restricts their ability to model a wide range of articulated objects in an open-vocabulary context.
To address these limitations, we propose \model, an automated framework that is able to convert any rigid 3D mesh into its articulated counterpart in an open-vocabulary manner. Given a 3D mesh, our framework utilizes advanced Vision-Language Models and visual prompting techniques to extract semantic information, allowing for both the segmentation of object parts and the construction of functional joints.
Our experiments show that \model~can generate large-scale, high-quality 3D articulated objects, including tools, toys, mechanical devices, and vehicles, significantly expanding the coverage of existing 3D articulated object datasets. Additionally, we show that these generated assets can facilitate the acquisition of new articulated object manipulation skills in simulation, which can then be transferred to a real robotic system.

Primary Area: generative models

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Submission Number: 951