Articraft: An Agentic System for Scalable Articulated 3D Asset Generation

2026-05-14

AI Summary

Existing datasets of articulated 3D objects suffer from limited scale and insufficient diversity, hindering the development of robust understanding models. To address this, this work proposes Articraft—a novel procedural agent framework for generating articulated 3D assets by reframing the generation task as code synthesis. Leveraging a domain-specific SDK, Articraft automatically produces programs that define object parts, geometric compositions, joint configurations, and validation tests, and iteratively refines them within a constrained space through a structured feedback mechanism. Using this approach, we construct Articraft-10K, a high-quality dataset comprising over 10,000 articulated 3D assets across 245 categories. The dataset demonstrates significant utility in downstream applications such as robotic simulation and virtual reality.

Abstract

A bottleneck in learning to understand articulated 3D objects is the lack of large and diverse datasets. In this paper, we propose to leverage large language models (LLMs) to close this gap and generate articulated assets at scale. We reduce the problem of generating an articulated 3D asset to that of writing a program that builds it. We then introduce a new agentic system, Articraft, that writes such programs automatically. We design a programmatic interface and harness to help the LLM do so effectively. The LLM writes code against a domain-specific SDK for defining parts, composing geometry, specifying joints, and writing tests to validate the resulting assets. The harness exposes a restricted workspace and interface to the LLM, validates the resulting assets, and returns structured feedback. In this way, the LLM is not distracted by details such as authoring a URDF file or managing a complex software environment. We show that this produces higher-quality assets than both state-of-the-art articulated-asset generators and general-purpose coding agents. Using Articraft, we build Articraft-10K, a curated dataset of over 10K articulated assets spanning 245 categories, and show its utility both for training models of articulated assets and in downstream applications such as robotics simulation and virtual reality.

Problem

• articulated 3D objects
• dataset scarcity
• scalable generation
• 3D asset generation

Innovation

• agentic system
• articulated 3D assets
• programmatic generation
• large language models
• domain-specific SDK