# EmoEdit: Evoking Emotions through Image Manipulation

arXiv: https://arxiv.org/abs/2405.12661 · HTML: https://mlsanity.com/paper/2405.12661/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 55 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 30 of 32)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [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)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [Compositional Visual Generation with Composable Diffusion Models](https://mlsanity.com/paper/2206.01714/) (2022)
- [MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing](https://mlsanity.com/paper/2306.10012/) (2023)
- [Zero-shot Image-to-Image Translation](https://mlsanity.com/paper/2302.03027/) (2023)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [EDICT: Exact Diffusion Inversion via Coupled Transformations](https://mlsanity.com/paper/2211.12446/) (2022)
- [InstructDiffusion: A Generalist Modeling Interface for Vision Tasks](https://mlsanity.com/paper/2309.03895/) (2023)
- [EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes](https://mlsanity.com/paper/2307.07961/) (2023)
- [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://mlsanity.com/paper/2210.05559/) (2022)
- [Language-Driven Image Style Transfer](https://mlsanity.com/paper/2106.00178/) (2021)
- [EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2401.04608/) (2024)
- [Learning Multi-level Deep Representations for Image Emotion Classification](https://mlsanity.com/paper/1611.07145/) (2016)
- [Stimuli-Aware Visual Emotion Analysis](https://mlsanity.com/paper/2109.01812/) (2021)
- [Learning by Planning: Language-Guided Global Image Editing](https://mlsanity.com/paper/2106.13156/) (2021)

## Built on by (most cited first, 30 of 30)

- [EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model](https://mlsanity.com/paper/2501.05710/) (2025)
- [UniEmo: Unifying Emotional Understanding and Generation with Learnable Expert Queries](https://mlsanity.com/paper/2507.23372/) (2025)
- [Affective Image Editing: Shaping Emotional Factors via Text Descriptions](https://mlsanity.com/paper/2505.18699/) (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)
- [EmoKGEdit: Training-free Affective Injection via Visual Cue Transformation](https://mlsanity.com/paper/2601.12326/) (2026)
- [EmoAgent: A Multi-Agent Framework for Diverse Affective Image Manipulation](https://mlsanity.com/paper/2503.11290/) (2025)
- [MUSE: Manipulating Unified Framework for Synthesizing Emotions in Images via Test-Time Optimization](https://mlsanity.com/paper/2511.21051/) (2025)
- [EmoStyle: Emotion-Driven Image Stylization](https://mlsanity.com/paper/2512.05478/) (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)
- [AIM-Bench: Benchmarking and Improving Affective Image Manipulation via Fine-Grained Hierarchical Control](https://mlsanity.com/paper/2604.10454/) (2026)
- [What Can I Edit? Open-Ended Strategy Discovery and the Emotion Editability Landscape](https://mlsanity.com/paper/2607.23920/) (2026)
- [EmoSpace: Immersive Affective Image Generation Guided by Fine-Grained Emotion Prototypes](https://mlsanity.com/paper/2602.11658/) (2026)
- [EmoStory: Emotion-Aware Story Generation](https://mlsanity.com/paper/2603.10349/) (2026)
- [MER-Bench: A Comprehensive Benchmark for Multimodal Meme Reappraisal](https://mlsanity.com/paper/2603.15020/) (2026)
- [EmoScene: A Dual-space Dataset for Controllable Affective Image Generation](https://mlsanity.com/paper/2604.00933/) (2026)
- [EmoWorld: A Decoupled Affective Field for Controllable Emotional Video Generation](https://mlsanity.com/paper/2608.06231/) (2026)
- [AffectDelta: Beyond Emotion Labels for Image Editing](https://mlsanity.com/paper/2609.02616/) (2026)
- [Seeing What You Say: Expressive Image Generation from Speech](https://mlsanity.com/paper/2511.03423/) (2025)
- [EmoLat: Text-driven Image Sentiment Transfer via Emotion Latent Space](https://mlsanity.com/paper/2601.12079/) (2026)
- [Towards LLM-centric Affective Visual Customization via Efficient and Precise Emotion Manipulating](https://mlsanity.com/paper/2602.18016/) (2026)
- [Emotional regulation improves deep learning-based image classification](https://mlsanity.com/paper/2606.13081/) (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)
- [What We Talk About When We Talk About Emotion Editing](https://mlsanity.com/paper/2610.13558/) (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)

Affective Image Manipulation (AIM) seeks to modify user-provided images to evoke specific emotional responses. This task is inherently complex due to its twofold objective: significantly evoking the intended emotion, while preserving the original image composition. Existing AIM methods primarily adjust color and style, often failing to elicit precise and profound emotional shifts. Drawing on psychological insights, we introduce EmoEdit, which extends AIM by incorporating content modifications to enhance emotional impact. Specifically, we first construct EmoEditSet, a large-scale AIM dataset comprising 40,120 paired data through emotion attribution and data construction. To make existing generative models emotion-aware, we design the Emotion adapter and train it using EmoEditSet. We further propose an instruction loss to capture the semantic variations in data pairs. Our method is evaluated both qualitatively and quantitatively, demonstrating superior performance compared to existing state-of-the-art techniques. Additionally, we showcase the portability of our Emotion adapter to other diffusion-based models, enhancing their emotion knowledge with diverse semantics.