# EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model

arXiv: https://arxiv.org/abs/2501.05710 · HTML: https://mlsanity.com/paper/2501.05710/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 63 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://mlsanity.com/paper/2404.02905/) (2024)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation](https://mlsanity.com/paper/2402.17245/) (2024)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges](https://mlsanity.com/paper/2202.10659/) (2022)
- [A Tutorial on Kernel Density Estimation and Recent Advances](https://mlsanity.com/paper/1704.03924/) (2017)
- [A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances](https://mlsanity.com/paper/2203.06935/) (2022)
- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (2021)
- [VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling](https://mlsanity.com/paper/2408.01181/) (2024)
- [High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning](https://mlsanity.com/paper/2305.02572/) (2023)
- [EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2401.04608/) (2024)
- [EmoEdit: Evoking Emotions through Image Manipulation](https://mlsanity.com/paper/2405.12661/) (2024)
- [MDAN: Multi-level Dependent Attention Network for Visual Emotion Analysis](https://mlsanity.com/paper/2203.13443/) (2022)
- [FindingEmo: An Image Dataset for Emotion Recognition in the Wild](https://mlsanity.com/paper/2402.01355/) (2024)
- [Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models](https://mlsanity.com/paper/2308.11578/) (2023)
- [Multi-modal Emotion Estimation for in-the-wild Videos](https://mlsanity.com/paper/2203.13032/) (2022)

## Built on by (most cited first, 26 of 26)

- [UniEmo: Unifying Emotional Understanding and Generation with Learnable Expert Queries](https://mlsanity.com/paper/2507.23372/) (2025)
- [MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network](https://mlsanity.com/paper/2503.12623/) (2025)
- [EmoVerse: A MLLMs-Driven Emotion Representation Dataset for Interpretable Visual Emotion Analysis](https://mlsanity.com/paper/2511.12554/) (2025)
- [EmoFeedback2: Reinforcement of Continuous Emotional Image Generation via LVLM-based Reward and Textual Feedback](https://mlsanity.com/paper/2511.19982/) (2025)
- [EmoCtrl: Controllable Emotional Image Content Generation](https://mlsanity.com/paper/2512.22437/) (2025)
- [TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models](https://mlsanity.com/paper/2601.08011/) (2026)
- [MUSE: Manipulating Unified Framework for Synthesizing Emotions in Images via Test-Time Optimization](https://mlsanity.com/paper/2511.21051/) (2025)
- [Emotion-Director: Bridging Affective Shortcut in Emotion-Oriented Image Generation](https://mlsanity.com/paper/2512.19479/) (2025)
- [Regressor-Guided Image Editing Shifts Emotion and Disengagement Timing in Social Media](https://mlsanity.com/paper/2501.12289/) (2025)
- [What Can I Edit? Open-Ended Strategy Discovery and the Emotion Editability Landscape](https://mlsanity.com/paper/2607.23920/) (2026)
- [Evaluating Human-LLM Representation Alignment: A Case Study on Affective Sentence Generation for Augmentative and Alternative Communication](https://mlsanity.com/paper/2503.11881/) (2025)
- [EmoSpace: Immersive Affective Image Generation Guided by Fine-Grained Emotion Prototypes](https://mlsanity.com/paper/2602.11658/) (2026)
- [CogBlender: Towards Continuous Cognitive Intervention in Text-to-Image Generation](https://mlsanity.com/paper/2603.09286/) (2026)
- [EmoStory: Emotion-Aware Story Generation](https://mlsanity.com/paper/2603.10349/) (2026)
- [EmoScene: A Dual-space Dataset for Controllable Affective Image Generation](https://mlsanity.com/paper/2604.00933/) (2026)
- [EPIG: Emotion-Based Prompting for Personalised Image Generation](https://mlsanity.com/paper/2606.13247/) (2026)
- [EmoWorld: A Decoupled Affective Field for Controllable Emotional Video Generation](https://mlsanity.com/paper/2608.06231/) (2026)
- [MultiEmo-Bench: Multi-label Visual Emotion Analysis for Multi-modal Large Language Models](https://mlsanity.com/paper/2605.14635/) (2026)
- [Controlla: Learning Controllability via Graph-Constrained Latent Geometry](https://mlsanity.com/paper/2605.16603/) (2026)
- [Bring Music The Horizon: Music-Driven 360^circ Video Generation](https://mlsanity.com/paper/2607.13471/) (2026)
- [Sense it with your eyes: Sensation Generation and Understanding for Advertisements](https://mlsanity.com/paper/2607.25314/) (2026)
- [Balancing Emotional Alignment and Semantic Consistency in Image Generation via Reinforcement Learning with Valence-Arousal Anchoring](https://mlsanity.com/paper/2609.12830/) (2026)
- [Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video](https://mlsanity.com/paper/2609.21624/) (2026)
- [Beyond Emotion Prompts: Fine-Grained Text-to-Image Generation Driven by Valence-Arousal-Dominance](https://mlsanity.com/paper/2609.24215/) (2026)
- [Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers](https://mlsanity.com/paper/2610.16892/) (2026)
- [EmoGenie: Benchmarking and Evaluating Expressed and Evoked Emotion in T2I Generation with MLLMs](https://mlsanity.com/paper/2610.18455/) (2026)

## Abstract (arXiv)

Recent research shows that emotions can enhance users' cognition and influence information communication. While research on visual emotion analysis is extensive, limited work has been done on helping users generate emotionally rich image content. Existing work on emotional image generation relies on discrete emotion categories, making it challenging to capture complex and subtle emotional nuances accurately. Additionally, these methods struggle to control the specific content of generated images based on text prompts. In this work, we introduce the new task of continuous emotional image content generation (C-EICG) and present EmotiCrafter, an emotional image generation model that generates images based on text prompts and Valence-Arousal values. Specifically, we propose a novel emotion-embedding mapping network that embeds Valence-Arousal values into textual features, enabling the capture of specific emotions in alignment with intended input prompts. Additionally, we introduce a loss function to enhance emotion expression. The experimental results show that our method effectively generates images representing specific emotions with the desired content and outperforms existing techniques.