# EmoSpace: Immersive Affective Image Generation Guided by Fine-Grained Emotion Prototypes arXiv: https://arxiv.org/abs/2602.11658 ยท HTML: https://mlsanity.com/paper/2602.11658/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 40 of its 64 references that are in the map. - First posted on arXiv 12 Feb 2026. ## Builds on (most cited first, 30 of 40) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (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) - [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023) - [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022) - [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024) - [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023) - [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022) - [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018) - [Neural Prototype Trees for Interpretable Fine-grained Image Recognition](https://mlsanity.com/paper/2012.02046/) (2020) - [Infinite Photorealistic Worlds using Procedural Generation](https://mlsanity.com/paper/2306.09310/) (2023) - [Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning](https://mlsanity.com/paper/2406.11161/) (2024) - [EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes](https://mlsanity.com/paper/2307.07961/) (2023) - [SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections](https://mlsanity.com/paper/2302.01330/) (2023) - [All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda](https://mlsanity.com/paper/2110.05352/) (2021) - [A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances](https://mlsanity.com/paper/2203.06935/) (2022) - [LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation](https://mlsanity.com/paper/2408.13252/) (2024) - [Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks](https://mlsanity.com/paper/1509.06041/) (2015) - [EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning](https://mlsanity.com/paper/2404.16670/) (2024) - [GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition](https://mlsanity.com/paper/2312.04293/) (2023) - [EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2401.04608/) (2024) - [It is Okay to Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection](https://mlsanity.com/paper/2204.07660/) (2022) - [Matrix-3D: Omnidirectional Explorable 3D World Generation](https://mlsanity.com/paper/2508.08086/) (2025) - [EmoEdit: Evoking Emotions through Image Manipulation](https://mlsanity.com/paper/2405.12661/) (2024) - [EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model](https://mlsanity.com/paper/2501.05710/) (2025) - [DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes](https://mlsanity.com/paper/2412.11100/) (2024) - [PanoWan: Lifting Diffusion Video Generation Models to 360deg with Latitude/Longitude-aware Mechanisms](https://mlsanity.com/paper/2505.22016/) (2025) - [Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition](https://mlsanity.com/paper/2505.19694/) (2025) - [Make Me Happier: Evoking Emotions Through Image Diffusion Models](https://mlsanity.com/paper/2403.08255/) (2024) ## Built on by (most cited first, 1 of 1) - [EmoScene: A Dual-space Dataset for Controllable Affective Image Generation](https://mlsanity.com/paper/2604.00933/) (2026) ## Abstract (arXiv) Immersive affective content generation aims to create visually compelling VR imagery with controllable emotional nuance, yet existing methods typically rely on coarse labels or prompt-only control. Although modern diffusion transformers (DiTs) such as FLUX improve visual fidelity, they are not designed to incorporate structured affective representations. We present EmoSpace, an immersive affective content generation framework guided by fine-grained emotion prototypes, transforming free-form text and emotion descriptions into fine-grained affective imagery through three coordinated components. First, to represent sub-emotion variation beyond conventional categorical models, EmoSpace learns a hierarchical bank of 256 prototypes with input-conditioned adaptation through vision-language alignment. Second, Prototype-Conditioned Steering converts these prototypes into DiT-compatible generation signals through multi-pathway injection and temporal blending, while Iterative Prompt Refinement enriches prompts with prototype-aligned sub-emotion descriptors. Third, Affect-Grounded Modulation coordinates emotion conditioning with controllable LoRAs for panoramic, stylized, and multi-conditional generation. Through quantitative and qualitative evaluations, EmoSpace improves fine-grained emotional alignment while maintaining high aesthetic quality. Our user study shows that EmoSpace outputs are perceived as more emotionally aligned than baseline results and more suitable for immersive scene design. Additionally, we find that immersive presentation alters emotional perception and increases emotional engagement. Together, these findings inform the design of emotion-aware generative systems for immersive media, with potential applications including education, immersive storytelling, and artistic creation. We will release our code and models to facilitate future research along this line.