EmoStyle: Emotion-Driven Image Stylization

TL;DR

EmoStyle introduces an emotion-driven image stylization framework that enhances artistic images with specific emotions, using a new dataset and novel modules to improve emotional expressiveness while preserving content.

Contribution

The paper presents EmoStyle, a novel framework with a new dataset and modules for emotion-driven image stylization, addressing data scarcity and style-emotion mapping challenges.

Findings

EmoStyle effectively enhances emotional expressiveness in stylized images.

The framework maintains content consistency while applying emotional styles.

User studies confirm improved emotional impact and visual quality.

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.