EmoStyle: Emotion-Driven Image Stylization

TL;DR

EmoStyle introduces Affective Image Stylization (AIS) to evoke specific emotions through artistic styles while preserving content. It builds EmoStyleSet from ArtEmis and proposes a two-component framework—Emotion–Content Reasoner and Style Quantizer with discrete style prototypes—trained in two stages to align emotion with style. Extensive quantitative, qualitative, and user-study evaluations demonstrate that EmoStyle achieves strong emotion fidelity and content preservation, with a flexible architecture adaptable to emotion-driven text-to-image generation. The work advances affective art by linking emotion concepts to interpretable style dictionaries and multimodal reasoning, enabling broader creative applications.

Abstract

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.

Figures (9)

• Figure 1: Affective Image Stylization with EmoStyle, aiming to transform user-provided images through artistic stylization to evoke specific emotional responses. It requires only emotion words as prompts, eliminating the need for reference images or detailed text descriptions.
• Figure 2: Comparison among Style Transfer (ST), Affective Image Manipulation (AIM), and our proposed Affective Image Stylization (AIS). While ST focuses on style and AIM emphasizes emotion, AIS generates emotionally expressive and stylized results.
• Figure 3: Construction process of EmoStyleSet. Given artworks from ArtEmis, after generation and filtering, each triplet contains a content image, a target emotion, and a stylized image.
• Figure 4: Overview of EmoStyle. We introduce an Emotion–Content Reasoner to integrate emotion and content features, and a Style Quantizer to map continuous queries to discrete style prototypes, generating stylized images with faithful emotion and preserved content.
• Figure 5: Comparison with the state-of-the-art methods, where EmoStyle surpasses others on emotion fidelity and aesthetic appeal.