Articraft: 一种可扩展的拟人化3D资产生成代理系统
Articraft: An Agentic System for Scalable Articulated 3D Asset Generation
AI总结：
本文提出Articraft代理系统，利用大语言模型生成大规模拟人化3D资产，构建了包含10000+资产的Articraft-10K数据集，应用于机器人仿真和虚拟现实等下游任务。
摘要：
在学习理解拟人化3D对象时，缺乏大规模和多样化的数据集是一个瓶颈。本文提出利用大语言模型（LLMs）来填补这一空白，生成大规模拟人化资产。我们将生成拟人化3D资产的问题转化为编写构建它的程序的问题。我们引入了一个新的代理系统Articraft，自动编写此类程序。我们设计了一个程序接口和工具包，帮助LLM有效完成此任务。LLM通过领域特定的SDK定义部件、组合几何体、指定关节并编写测试来验证生成的资产。工具包暴露了一个受限的工作空间和接口给LLM，验证生成的资产，并返回结构化的反馈。这样，LLM不会被诸如编写URDF文件或管理复杂软件环境等细节所干扰。我们证明，这种方法生成的资产质量优于最先进的拟人化资产生成器和通用编程代理。使用Articraft，我们构建了Articraft-10K，一个包含超过10000个资产的精选数据集，涵盖245个类别，并展示了其在训练拟人化资产模型和下游应用如机器人仿真和虚拟现实中的用途。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
