AceTone：连接词语与颜色的条件图像评分
AceTone: Bridging Words and Colors for Conditional Image Grading
AI总结：
AceTone提出了一种基于多模态条件的颜色评分方法，通过生成颜色转换任务实现统一框架，利用VQ-VAE tokenizer压缩LUT向量并结合大规模数据集训练，提升图像评分的感知和美学一致性。
摘要：
颜色影响我们对图像风格和情感的解读。以往的颜色评分方法依赖于基于块的重新着色或固定滤波器组，难以在创意意图上推广或与人类审美偏好对齐。本文提出AceTone，首个支持多模态条件颜色评分的统一框架。AceTone将评分建模为生成颜色转换任务，其中模型直接根据文本提示或参考图像生成3D-LUTs。我们开发了基于VQ-VAE的分词器，将3×32³ LUT向量压缩到64个离散令牌，具有ΔE<2的保真度。我们进一步构建了大规模数据集AceTone-800K，并训练视觉-语言模型预测LUT令牌，随后通过强化学习对齐输出与感知保真度和美学。实验表明，AceTone在文本引导和参考图像引导评分任务中均取得最佳性能，比现有方法提升LPIPS高达50%。人类评估证实，AceTone的结果在视觉上令人满意且风格上一致，展示了语言驱动、美学对齐颜色评分的新路径。
英文摘要：
Color affects how we interpret image style and emotion. Previous color grading methods rely on patch-wise recoloring or fixed filter banks, struggling to generalize across creative intents or align with human aesthetic preferences. In this study, we propose AceTone, the first approach that supports multimodal conditioned color grading within a unified framework. AceTone formulates grading as a generative color transformation task, where a model directly produces 3D-LUTs conditioned on text prompts or reference images. We develop a VQ-VAE based tokenizer which compresses a $3\times32^3$ LUT vector to 64 discrete tokens with $ΔE<2$ fidelity. We further build a large-scale dataset, AceTone-800K, and train a vision-language model to predict LUT tokens, followed by reinforcement learning to align outputs with perceptual fidelity and aesthetics. Experiments show that AceTone achieves state-of-the-art performance on both text-guided and reference-guided grading tasks, improving LPIPS by up to 50% over existing methods. Human evaluations confirm that AceTone's results are visually pleasing and stylistically coherent, demonstrating a new pathway toward language-driven, aesthetic-aligned color grading.
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。
