LumiVideo：一种用于视频色彩分级的智能代理系统
LumiVideo: An Intelligent Agentic System for Video Color Grading
AI总结：
LumiVideo通过感知、推理、执行和反思四个阶段模仿专业调色师的工作流程，利用LLM和RAG框架实现非线性色彩参数空间导航，生成符合行业标准的色彩配置文件，并支持通过自然语言反馈进行迭代优化。
摘要：
视频色彩分级是将原始素材转化为具有情感共鸣的电影视觉的关键后期制作过程。现有自动化方法作为静态黑盒执行器，直接输出编辑像素，缺乏可解释性和专业所需的迭代控制。我们引入LumiVideo，一种模仿专业调色师认知流程的代理系统，通过感知、推理、执行和反思四个阶段，仅需原始log编码视频即可自动生成电影基础调色等级。其推理引擎结合LLM内部化的电影知识与检索增强生成（RAG）框架，通过树状思维（ToT）搜索导航非线性色彩参数空间。而非生成像素，系统将推导出的参数编译为行业标准的ASC-CDL配置和全局一致的3D LUT，确保时间一致性。可选的反思循环允许创作者通过自然语言反馈进行结果优化。我们进一步引入LumiGrade，首个用于评估自动化调色的log编码视频基准。实验表明，LumiVideo在完全自动模式下接近人类专家质量，同时在受指导时能够实现精确的迭代控制。
英文摘要：
Video color grading is a critical post-production process that transforms flat, log-encoded raw footage into emotionally resonant cinematic visuals. Existing automated methods act as static, black-box executors that directly output edited pixels, lacking both interpretability and the iterative control required by professionals. We introduce LumiVideo, an agentic system that mimics the cognitive workflow of professional colorists through four stages: Perception, Reasoning, Execution, and Reflection. Given only raw log video, LumiVideo autonomously produces a cinematic base grade by analyzing the scene's physical lighting and semantic content. Its Reasoning engine synergizes an LLM's internalized cinematic knowledge with a Retrieval-Augmented Generation (RAG) framework via a Tree of Thoughts (ToT) search to navigate the non-linear color parameter space. Rather than generating pixels, the system compiles the deduced parameters into industry-standard ASC-CDL configurations and a globally consistent 3D LUT, analytically guaranteeing temporal consistency. An optional Reflection loop then allows creators to refine the result via natural language feedback. We further introduce LumiGrade, the first log-encoded video benchmark for evaluating automated grading. Experiments show that LumiVideo approaches human expert quality in fully automatic mode while enabling precise iterative control when directed.
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