EmoStyle：情感驱动的图像风格化
EmoStyle: Emotion-Driven Image Stylization
把「情感」作为风格迁移的条件信号，并用向量量化把连续风格特征离散成情感相关码本条目，这个 emotion-aware style dictionary 可直接作为其他生成任务（如 T2I、编辑）的可插拔风格条件模块复用。
原文依据 we further develop a Style Quantizer that converts continuous style features into emotion-related codebook entries
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论艺术长期以来一直是表达情感的深刻媒介。现有图像风格化方法虽能有效改变视觉外观，却常常忽视风格所承载的情感影响。为弥合这一差距，我们提出情感图像风格化（Affective Image Stylization, AIS）任务，即在保持内容的同时施加艺术风格以唤起特定情感。我们提出 EmoStyle 框架，以应对 AIS 中的关键挑战，包括训练数据缺乏与情感—风格映射问题。首先，我们基于 ArtEmis 构建了 EmoStyleSet，这是一个内容—情感—风格化图像三元组数据集，用于支撑 AIS。随后，我们提出情感—内容推理器（Emotion-Content Reasoner），它自适应地融合情感线索与内容，以学习连贯的风格查询。鉴于艺术风格的离散特性，我们进一步设计了风格量化器（Style Quantizer），将连续风格特征转换为与情感相关的码本条目。大量定性与定量评估（包括用户研究）表明，EmoStyle 在保持内容一致性的同时增强了情感表现力。此外，所学到的情感感知风格字典可适配于其他生成任务，凸显了其更广泛的应用潜力。我们的工作为情感驱动的图像风格化奠定了基础，拓展了 AI 生成艺术的创作潜能。
英文原文摘要 1353 字符
Art has long been a profound medium for expressing emotions.While existing image stylization methods effectively transform visual appearance, they often overlook the emotional impact carried by styles.To bridge this gap, we introduce Affective Image Stylization (AIS), a task that applies artistic styles to evoke specific emotions while preserving content.We present EmoStyle, a framework designed to address key challenges in AIS, including the lack of training data and the emotion-style mapping.First, we construct EmoStyleSet, a content-emotion-stylized image triplet dataset derived from ArtEmis to support AIS.We then propose an Emotion-Content Reasoner that adaptively integrates emotional cues with content to learn coherent style queries.Given the discrete nature of artistic styles, we further develop a Style Quantizer that converts continuous style features into emotion-related codebook entries.Extensive qualitative and quantitative evaluations, including user studies, demonstrate that EmoStyle enhances emotional expressiveness while maintaining content consistency.Moreover, the learned emotion-aware style dictionary is adaptable to other generative tasks, highlighting its potential for broader applications.Our work establishes a foundation for emotion-driven image stylization, expanding the creative potential of AI-generated art.