智能体计算机图形学进展：统一分类与文献系统
原标题：Paper page - Advances in Agentic Computer Graphics
AI 摘要
该论文提出了一套面向LLM驱动的智能体计算机图形学（agentic computer graphics）的统一分类体系和文献系统，用于分析人机协作与长程规划的趋势与开放挑战。作者指出，LLM智能体已能生成图形代码并通过反馈迭代改进，这催生了围绕智能体系统构建图形算法的新范式。由于该领域研究快速增长、术语不统一，人工文献综述难以跟上，因此他们构建了可大规模辅助研究者分析新旧论文的智能体文献系统，并讨论了智能体与工具协调、人机协作接口设计、长程规划与精炼以及交互式图形系统可靠评估等开放问题。
正文节选
Abstract This report proposes a unified taxonomy and literature system for agentic computer graphics driven by large language models, analyzing trends and open challenges in human-agent collaboration and long-horizon planning. Computer graphics is built on code. Large language model (LLM) agents can now generate graphics code and iteratively refine it through various feedback. This capability opens the door to a potentially new paradigm of computer graphics, with graphics algorithms increasingly