EmoStyle: Emotion-Driven Image Stylization

• URL: http://arxiv.org/abs/2512.05478v1
• Date: Fri, 05 Dec 2025 07:15:02 GMT
• Title: EmoStyle: Emotion-Driven Image Stylization
• Abstract summary: Affective Image Stylization (AIS) is a task that applies artistic styles to evoke specific emotions while preserving content.<n>We present EmoStyle, a framework designed to address key challenges in AIS, including the lack of training data and the emotion-style mapping.<n>Our work establishes a foundation for emotion-driven image stylization, expanding the creative potential of AI-generated art.
• Score: 8.734768359185106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• EmoStyle: Affective Conditioning of Style-Specialist Experts for Emotional Image Generation [50.22045436428303]
  EmoStyle is a Z-Image-based framework that converts the input prompt into a structured generation state.<n>We train a dedicated LoRA adapter for each artistic style bucket and select the corresponding expert during inference.<n>A lightweight VLM-guided candidate selection step ranks the generated images based on prompt alignment, style consistency, emotional expression, and visual quality.
  arXiv  Detail & Related papers  (2026-07-11T07:12:07Z)
• EmoCtrl: Controllable Emotional Image Content Generation [9.677863079897735]
  We introduce Controllable Emotional Image Content Generation (C-EICG)<n>C-EICG aims to generate images that remain faithful to a given content description while expressing a target emotion.<n>EmoCtrl is supported by a dataset annotated with content, emotion, and affective prompts.
  arXiv  Detail & Related papers  (2025-12-27T02:18:36Z)
• EmoVerse: A MLLMs-Driven Emotion Representation Dataset for Interpretable Visual Emotion Analysis [61.87711517626139]
  EmoVerse is a large-scale open-source dataset that enables interpretable visual emotion analysis.<n>With over 219k images, the dataset further includes dual annotations in Categorical Emotion States (CES) and Dimensional Emotion Space (DES)
  arXiv  Detail & Related papers  (2025-11-16T11:16:50Z)
• CoEmoGen: Towards Semantically-Coherent and Scalable Emotional Image Content Generation [3.5418954219513625]
  Emotional Image Content Generation (EICG) aims to generate semantically clear and emotionally faithful images based on given emotion categories.<n>We propose CoEmoGen, a novel pipeline notable for its semantic coherence and high scalability.<n>To intuitively showcase scalability, we curate EmoArt, a large-scale dataset of emotionally evocative artistic images.
  arXiv  Detail & Related papers  (2025-08-05T15:04:34Z)
• EmoArt: A Multidimensional Dataset for Emotion-Aware Artistic Generation [16.77813911200113]
  We present the EmoArt dataset -- one of the most comprehensive emotion-annotated art datasets to date.<n>It contains 132,664 artworks across 56 painting styles, offering rich stylistic and cultural diversity.<n>Using EmoArt, we evaluate popular text-to-image diffusion models for their ability to generate emotionally aligned images from text.
  arXiv  Detail & Related papers  (2025-06-04T07:43:51Z)
• EmoEdit: Evoking Emotions through Image Manipulation [62.416345095776656]
  Affective Image Manipulation (AIM) seeks to modify user-provided images to evoke specific emotional responses.<n>We introduce EmoEdit, which extends AIM by incorporating content modifications to enhance emotional impact.<n>Our method is evaluated both qualitatively and quantitatively, demonstrating superior performance compared to existing state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-05-21T10:18:45Z)
• EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models [11.901294654242376]
  We introduce Emotional Image Content Generation (EICG), a new task to generate semantic-clear and emotion-faithful images given emotion categories. Specifically, we propose an emotion space and construct a mapping network to align it with the powerful Contrastive Language-Image Pre-training (CLIP) space. Our method outperforms the state-of-the-art text-to-image approaches both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2024-01-09T15:23:21Z)
• Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling [50.99252242917458]
  Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting. To address the issue of data scarcity, we meticulously create emotional labels in terms of category and intensity. Our model outperforms the baseline models in understanding and rendering emotions.
  arXiv  Detail & Related papers  (2023-12-19T08:47:50Z)
• Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
  We present Impressions, a novel dataset through which to investigate the semiotics of images. We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images. This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
  arXiv  Detail & Related papers  (2023-10-27T04:30:18Z)
• StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning [69.06749934902464]
  We propose a style-guided high-order attention network for image emotion distribution learning termed StyleEDL. StyleEDL interactively learns stylistic-aware representations of images by exploring the hierarchical stylistic information of visual contents. In addition, we introduce a stylistic graph convolutional network to dynamically generate the content-dependent emotion representations.
  arXiv  Detail & Related papers  (2023-08-06T03:22:46Z)
• EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes [53.95428298229396]
  We introduce EmoSet, the first large-scale visual emotion dataset annotated with rich attributes. EmoSet comprises 3.3 million images in total, with 118,102 of these images carefully labeled by human annotators. Motivated by psychological studies, in addition to emotion category, each image is also annotated with a set of describable emotion attributes.
  arXiv  Detail & Related papers  (2023-07-16T06:42:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.