3Dify：一种采用MCP与RAG的大语言模型辅助程序化3D-CG生成框架
3Dify: a Framework for Procedural 3D-CG Generation Assisted by LLMs Using MCP and RAG
AI总结：
本研究提出3Dify框架，基于Dify结合MCP、RAG等技术，通过自然语言指令驱动DCC工具生成3D-CG，支持CUA操作GUI、偏好反馈优化、本地LLM集成，降低生成成本。
摘要：
本文提出了“3Dify”——一种利用大语言模型（LLMs）的程序化3D计算机图形（3D-CG）生成框架。该框架支持用户仅通过自然语言指令即可生成3D-CG内容。3Dify基于开源AI应用开发平台Dify构建，整合了模型上下文协议（MCP）、检索增强生成（RAG）等多种前沿大语言模型相关技术。\n为支持3D-CG生成，3Dify通过MCP实现各类数字内容创作（DCC）工具的自动化操作。当DCC工具不支持基于MCP的交互时，框架采用计算机操作智能体（CUA）方法实现图形用户界面（GUI）操作自动化。此外，为提升图像生成质量，3Dify允许用户从多个候选结果中选择偏好图像提供反馈，大语言模型会从这些选择中学习变量模式并应用于后续生成。同时，3Dify支持集成本地部署的大语言模型，用户可使用自定义开发的模型，并借助自身计算资源减少外部API调用产生的时间与金钱成本。
英文摘要：
This paper proposes "3Dify," a procedural 3D computer graphics (3D-CG) generation framework utilizing Large Language Models (LLMs). The framework enables users to generate 3D-CG content solely through natural language instructions. 3Dify is built upon Dify, an open-source platform for AI application development, and incorporates several state-of-the-art LLM-related technologies such as the Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG). For 3D-CG generation support, 3Dify automates the operation of various Digital Content Creation (DCC) tools via MCP. When DCC tools do not support MCP-based interaction, the framework employs the Computer-Using Agent (CUA) method to automate Graphical User Interface (GUI) operations. Moreover, to enhance image generation quality, 3Dify allows users to provide feedback by selecting preferred images from multiple candidates. The LLM then learns variable patterns from these selections and applies them to subsequent generations. Furthermore, 3Dify supports the integration of locally deployed LLMs, enabling users to utilize custom-developed models and to reduce both time and monetary costs associated with external API calls by leveraging their own computational resources.
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。
