1. Bibliographic Information

• Title: EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models
• Authors: Jingyuan Yang, Jiawei Feng, Hui Huang
• Affiliations: Shenzhen University
• Journal/Conference: This paper is a preprint available on arXiv. It has not yet been published in a peer-reviewed journal or conference at the time of this analysis.
• Publication Year: 2024 (Submitted to arXiv on January 8, 2024)
• Abstract: The paper addresses a key limitation of modern text-to-image diffusion models: their difficulty in generating images based on abstract concepts like emotions, despite their proficiency with concrete objects. The authors introduce a new task called Emotional Image Content Generation (EICG), which aims to generate images with clear semantics that faithfully evoke a specified emotion. To achieve this, they propose a novel method that first defines an emotion space to represent emotional relationships. A mapping network is then used to align this space with the powerful semantic space of CLIP. To ensure the quality of generated images, the method incorporates an attribute loss for semantic diversity and emotion confidence to maintain emotional fidelity. The paper introduces three custom metrics (emotion accuracy, semantic clarity, semantic diversity) to evaluate performance, demonstrating that their approach outperforms state-of-the-art models. Beyond generation, the method also shows potential for applications in emotion understanding and art design.
• Original Source Link:
  • ArXiv: https://arxiv.org/abs/2401.04608
  • PDF: http://arxiv.org/pdf/2401.04608v1

2. Executive Summary

• Background & Motivation (Why):

  • Core Problem: State-of-the-art text-to-image diffusion models (like Stable Diffusion) excel at creating photorealistic images from text prompts describing concrete objects (e.g., "a dog on a skateboard"). However, they struggle to generate images that effectively convey abstract concepts, particularly emotions (e.g., "sadness," "joy").
  • Gaps in Prior Work: Previous attempts to create emotional images, known as image emotion transfer, focused on modifying the color and style of an existing image. This approach is fundamentally limited because the core content of the image remains fixed, resulting in only subtle emotional shifts and low accuracy. Psychological studies show that emotions are often triggered by specific semantic content (e.g., a cemetery for sadness, a party for amusement), a factor largely ignored by these methods.
  • Fresh Angle: Instead of modifying an existing image, this paper proposes generating entirely new emotional image content from scratch. The core idea is to teach a model what to draw to evoke a certain emotion, rather than just how to color or style it. This introduces the new task of Emotional Image Content Generation (EICG).

• Main Contributions / Findings (What):

  • A New Task and Metrics: The paper formally introduces EICG, the task of generating semantically clear and emotionally faithful images from an emotion category. To measure success, it proposes three custom evaluation metrics: emotion accuracy (Emo-A), semantic clarity (Sem-C), and semantic diversity (Sem-D).
  • A Novel Generation Framework: The core of the proposed method, EmoGen, involves three key components:
    1. An emotion space that explicitly models the relationships between different emotions.
    2. A mapping network that bridges the abstract emotion space with the semantically rich CLIP space, effectively translating an abstract emotion into concrete visual concepts.
    3. A specialized loss function combining an attribute loss (to encourage diverse and clear objects/scenes) and emotion confidence (to filter out emotionally neutral content).
  • Superior Performance: EmoGen is shown to outperform leading text-to-image models like Stable Diffusion, Textual Inversion, and DreamBooth across five different metrics, including both standard and the newly proposed ones.
  • New Applications: The learned emotional representations enable novel applications, including emotion decomposition (identifying key concepts for an emotion), emotion transfer (applying emotional content to neutral scenes), and emotion fusion (combining multiple emotions).

3. Prerequisite Knowledge & Related Work

• Foundational Concepts:

  • Diffusion Models: A class of generative models that create data (like images) by learning to reverse a gradual noising process. They start with random noise and progressively "denoise" it, guided by a condition (e.g., a text prompt), until a clean image is formed. They are known for producing high-quality and diverse results.
  • CLIP (Contrastive Language-Image Pre-training): A powerful model from OpenAI trained on a massive dataset of image-text pairs. It learns a shared "embedding space" where a text description and its corresponding image are located close to each other. This allows it to understand the semantic relationship between language and visuals, making it a cornerstone of modern text-to-image models.
  • Latent Diffusion Models (LDM): An efficient variant of diffusion models, like the popular Stable Diffusion. Instead of operating on high-resolution pixel images, LDMs work in a compressed, lower-dimensional "latent" space. This drastically reduces computational cost while maintaining high generation quality.
  • Visual Emotion Analysis (VEA): A research field in computer vision that aims to automatically recognize the emotion conveyed by an image. This is typically framed as a classification task (e.g., labeling an image as "happy," "sad," etc.). This paper effectively tries to "reverse" VEA by generating an image for a given emotion label.

• Previous Works:

  • Visual Emotion Analysis (VEA): Early VEA methods relied on low-level features like color, texture, and composition. More recent deep learning approaches incorporate high-level semantics by identifying objects and scenes, acknowledging that what is in the image is crucial for evoking emotion. EmoGen builds on this insight by focusing on generating the right semantic content.
  • Text-to-Image Generation: The paper situates itself in the context of advanced models like Stable Diffusion (a general-purpose model), Textual Inversion, and DreamBooth (models for personalizing generation to specific subjects). While these models are powerful, the authors argue they are designed for concrete concepts and falter when given abstract emotional prompts.
  • Image Emotion Transfer: This is the most direct predecessor. These methods take an image and a target emotion, and modify the image's low-level properties (color palette, style) to match the emotion. Their main drawback, as highlighted by the paper, is the fixed content, which limits the emotional impact. For example, changing the color of a funeral photo to be bright and vibrant is unlikely to make it feel joyous. EmoGen's key innovation is generating new content, not just restyling it.

• Differentiation: EmoGen distinguishes itself by fundamentally shifting the problem from style modification to content generation. While image emotion transfer asks, "How can I make this picture of a forest look sadder?", EmoGen asks, "What kind of picture should I create to show sadness?". This content-centric approach directly addresses the limitations of previous work and aligns better with psychological principles of emotion evocation.

4. Methodology (Core Technology & Implementation)

The core of EmoGen is a two-stage process: first, learning a robust representation for emotions, and second, using this representation to guide a diffusion model to generate emotional content.

该图像是论文中EmoGen模型的整体流程示意图，展示了从输入情绪词和图像，经情绪编码器映射到情绪空间，再通过映射网络投射至CLIP空间，结合属性标签和情绪置信度进行联合优化的过程。优化公式为$L_{stage-1} = L_{emo}$和$L_{stage-2} = (1-\alpha_{ij})L_{LDM} + \alpha_{ij}L_{attr}$。

4.1. Emotion Representation

• The Problem with CLIP Space: The authors observe that while CLIP's embedding space is excellent for general semantics, it does not cluster emotions coherently. As shown in Figure 2, images that evoke "amusement" (e.g., a toy, an amusement park) can be far apart in CLIP space because their semantic content is different.

  该图像是论文中图2的示意图，比较了(a)情感空间与(b)CLIP空间在情感关系捕捉上的差异。情感空间展现出更明确的情绪聚类结构，而CLIP空间虽然语义丰富，却难以有效捕捉情感间的关系。

• The Solution: A Dedicated Emotion Space: To solve this, the authors first create a dedicated emotion space.

  • Training: They train an image encoder $\varphi$ (a ResNet-50 network) on the EmoSet dataset, which contains images labeled with one of eight emotions. The encoder is trained with a standard Cross-Entropy loss, called emotion loss ($\mathcal{L}_{emo}$). $$\mathcal { L } _ { e m o } = - \sum _ { i = 1 } ^ { C } y _ { e m o } \log \frac { \exp ( \varphi ( x , i ) ) } { \sum _ { i = 1 } ^ { C } \exp ( \varphi ( x , i ) ) }$$
    • Symbol Explanation:
      • $x$: The input image.
      • $y_{emo}$: The one-hot encoded ground-truth emotion label for the image.
      • $C$: The total number of emotion categories (8 in this case).
      • $\varphi(x, i)$: The output logit from the encoder for the $i$-th emotion class.
  • This training forces the encoder to produce representations where images with the same emotion are clustered together, creating the desired emotion space. During generation, the parameters of this encoder $\varphi$ are frozen.

4.2. Emotional Content Generation

The next challenge is to connect this abstract emotion space to the concrete, content-rich CLIP space used by diffusion models.

• Mapping Network: A simple linear mapping is insufficient because a single emotion corresponds to many different types of content. The authors design a mapping network ($F$) using a Multilayer Perceptron (MLP) with non-linear activation functions (ReLU). This allows a single point in the emotion space to be projected to multiple distinct locations in the CLIP space, enabling semantic diversity. The output is then passed through a frozen CLIP text transformer to produce the final textual embedding that guides the diffusion model.

• Limitations of Standard LDM Loss: The standard loss for training diffusion models, the LDM loss, aims to reconstruct the training image. $$\mathcal { L } _ { L D M } = \mathbb { E } _ { z , x , \epsilon , t } \left[ \left| \left| \epsilon - \epsilon _ { \theta } \left( z _ { t } , t , t _ { \theta } \left( F \left( \varphi \left( x \right) \right) \right) \right) \right| \right| _ { 2 } ^ { 2 } \right]$$

  • Symbol Explanation:
    • $\epsilon$: The random noise added to the latent image.
    • $\epsilon_{\theta}$: The denoising network (U-Net) that predicts the noise.
    • $z_t$: The noised latent image at timestep $t$.
    • $t_{\theta}(F(\varphi(x)))$: The text conditioning, derived from the input image $x$ via the emotion encoder $\varphi$, mapping network $F$, and text transformer $t_{\theta}$. The authors argue this loss alone is problematic for emotions because it can cause "mode collapse" (e.g., the model learns that "amusement" only means "amusement park") and focuses on low-level features like color rather than clear semantics (Figure 4a).

• Attribute Loss for Semantic Diversity: To force the model to learn diverse and clear content, an attribute loss ($\mathcal{L}_{attr}$) is introduced. This loss leverages object and scene labels (attributes) from the EmoSet dataset. It encourages the learned embedding for an emotion to be close to the CLIP embeddings of its associated attributes. $$\mathcal { L } _ { a t t r } = - \sum _ { j = 1 } ^ { K } y _ { a t t r } \log \frac { \exp ( f ( v _ { e m o } , \tau _ { \theta } ( a _ { j } ) ) ) } { \sum _ { j = 1 } ^ { K } \exp ( f ( v _ { e m o } , \tau _ { \theta } ( a _ { j } ) ) ) }$$ $$f \left( p , q \right) = { \frac { p \cdot q } { \| p \| \| q \| } }$$

  • Symbol Explanation:

    • $v_{emo}$: The learned CLIP embedding for the emotion.

    • $a_j$: The $j$-th attribute text (e.g., "beach", "dog").

    • $\tau_{\theta}(a_j)$: The CLIP text embedding for attribute $a_j$.

    • f(p, q): The cosine similarity between two vectors.

    • $y_{attr}$: The ground-truth label for the attribute.

    • $K$: The total number of attributes. This loss pushes the emotion representation towards a variety of relevant concepts, as seen in Figure 4b.

      该图像是一个示意图，展示了图4中损失函数设计的动机。分别对比了(a)仅使用LDM损失、(b)加入属性损失提升语义清晰度、(c)结合情感置信度确保情感准确性。文本中涉及的权重调整采用了$(1-\alpha_i)\mathcal{L}_{LDM} + \alpha_i\mathcal{L}_{attr}$。

• Emotion Confidence for Emotional Fidelity: A key insight is that not all attributes are equally emotional (e.g., "tree" is more neutral than "cemetery"). To address this, the authors propose emotion confidence ($\alpha_{ij}$), a score that measures how strongly attribute $j$ is associated with emotion $i$.

  该图像是图5，展示了情感置信度的示意图。(a)部分为不同语义属性的示例图片，(b)部分为对应的情感置信度分布，反映每个属性在八种情感上的置信比例变化。

  This score is pre-calculated by passing all images of a given attribute through a pre-trained emotion classifier and averaging the predictions. $$\alpha _ { i j } = \frac { 1 } { N _ { j } } \sum _ { n = 1 } ^ { N _ { j } } p \left( x _ { n } , i \right)$$

  • Symbol Explanation:
    • $\alpha_{ij}$: The emotion confidence of attribute $j$ for emotion $i$.
    • $N_j$: The number of images with attribute $j$.
    • $x_n$: The $n$-th image with attribute $j$.
    • $p(x_n, i)$: The predicted probability of emotion $i$ for image $x_n$ by a pre-trained classifier.

• Final Combined Loss: The emotion confidence score is then used as a dynamic weight to balance the LDM loss and attribute loss. $$\mathcal { L } = \left( 1 - \alpha _ { i j } \right) \mathcal { L } _ { L D M } + \alpha _ { i j } \mathcal { L } _ { a t t r }$$ If an attribute has high emotional confidence for a given emotion (high $\alpha_{ij}$), the model prioritizes the semantic attribute loss. If the confidence is low, it relies more on the pixel-level reconstruction from the LDM loss. This clever mechanism ensures the generated images are both semantically rich and emotionally faithful (Figure 4c).

5. Experimental Setup

• Datasets: The experiments use the EmoSet dataset, a large-scale visual emotion dataset containing 118,102 images. For this work, a subset containing images with explicit object or scene attribute labels is used to train the model, specifically to enable the attribute loss.

• Evaluation Metrics:

  1. FID (Fréchet Inception Distance): Measures the similarity between the distribution of generated images and real images. It primarily evaluates image fidelity and realism. A lower FID is better.
  2. LPIPS (Learned Perceptual Image Patch Similarity): Measures the diversity of the generated images by comparing the perceptual similarity between pairs of images. It evaluates image diversity. A higher LPIPS is better.
  3. Emo-A (Emotion Accuracy): A custom metric to measure emotion faithfulness. Generated images are fed into a pre-trained emotion classifier, and the accuracy is the percentage of images correctly classified with the target emotion. A higher Emo-A is better.
  4. Sem-C (Semantic Clarity): A custom metric to measure the unambiguity of the content. The paper states details are in the supplementary material, but it likely involves using a pre-trained object/scene classifier and measuring its prediction confidence on the generated images. A higher Sem-C indicates more recognizable content.
  5. Sem-D (Semantic Diversity): A custom metric to measure the richness of generated content for a single emotion. This likely involves classifying the objects/scenes in the generated images and measuring the variety or entropy of the predicted labels. A higher Sem-D is better.

• Baselines: The proposed method is compared against three strong text-to-image baselines:

  • Stable Diffusion: A general, powerful, pre-trained latent diffusion model.
  • Textual Inversion: A method for personalizing diffusion models by learning a new "word" in the embedding space for a specific concept from a few images.
  • DreamBooth: Another personalization method that fine-tunes the entire diffusion model to learn a new subject.

6. Results & Analysis

• Core Results:

  • Qualitative Comparisons: As shown in Figure 6, EmoGen produces images with significantly higher semantic diversity and clarity compared to the baselines. For "awe," EmoGen generates diverse majestic scenes (lakes, mountains, valleys), while baselines tend to produce repetitive textures or ambiguous imagery. For "anger," baselines often generate distorted tigers, whereas EmoGen produces a wider range of concepts like protests, flags, and guns.

    该图像是情感类别下多种图像生成方法的对比示意图，展示了“Awe（敬畏）”、“Anger（愤怒）”和“Contentment（满足）”三种情绪对应的真实图片和不同模型生成的图像。图中展示了不同方法在表达情感语义和图像清晰度上的差异，突出本文所提方法在情感准确性和语义多样性上的优势。

  • Quantitative Comparisons: The authors transcribe the results into Table 1. EmoGen outperforms all baselines on all five metrics. It achieves the lowest (best) FID, indicating higher image quality, and the highest LPIPS and Sem-D, confirming its superior ability to generate diverse content. It also leads in Emo-A and Sem-C, showing the generated images are both emotionally accurate and semantically clear.

    (Manual transcription of Table 1 from the paper)

    Method	FID ↓	LPIPS ↑	Emo-A ↑	Sem-C ↑	Sem-D ↑
    Stable Diffusion [38]	44.05	0.687	70.77%	0.608	0.0199
    Textual Inversion [10]	50.51	0.702	74.87%	0.605	0.0282
    DreamBooth [39]	46.89	0.661	70.50%	0.614	0.0178
    Ours	41.60	0.717	76.25%	0.633	0.0335
    w/o F	57.54	0.713	71.12%	0.615	0.0261
    w/o Lat	51.13	0.707	65.75%	0.592	0.0270
    w/o αj	43.30	0.714	74.88%	0.591	0.0263

  • User Study: A user study further validates the model's superiority. Participants preferred EmoGen's results over baselines for image fidelity, emotion faithfulness, and especially semantic diversity. Crucially, when asked why they felt a certain emotion from an image, 88.39% of responses pointed to the image content/semantics, strongly supporting the paper's core hypothesis.

    (Manual transcription of Table 2 from the paper)

    Method	Image fidelity ↑		Emotion faithfulness ↑	Semantic diversity ↑
    Stable Diffusion	67.86±15.08%	73.66±11.80%	87.88±9.64%
    Textual Inversion	79.91±16.92%	72.75±16.90%	85.66±10.51%
    DreamBooth	77.23±14.00%	80.79±8.64%	81.68±17.06%

• Ablation Study: The ablation studies in Table 1 confirm the importance of each proposed component:

  • w/o F: Removing the non-linear mapping network drastically worsens FID and Sem-D, showing it is crucial for mapping an emotion to diverse, high-quality semantics.
  • w/o L_attr: Removing the attribute loss (L_attr is a typo in the table, should be $\mathcal{L}_{attr}$) hurts Sem-C and Sem-D, demonstrating its role in generating clear and diverse content.
  • w/o α_ij: Removing the emotion confidence weight hurts Emo-A and Sem-C. This shows that without filtering for emotionally relevant attributes, the model generates clear but sometimes emotionally neutral content.

7. Conclusion & Reflections

• Applications: The paper showcases three promising applications of the learned emotion representations:

  • Emotion Decomposition (Figure 7): By finding the closest semantic concepts to a learned emotion embedding in CLIP space, the model can "decompose" an abstract emotion like "excitement" into concrete concepts like "surfboard," "bicycle," and "athletic field." This provides insight into how visual content triggers emotions.

    该图像是图7的示意图，展示了情绪词“excitemenet”的分解过程。(a)部分细化为多个反映情绪语义的情绪概念，(b)部分对应展示了与这些情绪概念相关的生成图像样本。

  • Emotion Transfer (Figure 8a): The learned emotion representations can be applied to neutral concepts (like "a room" or "a cat") to generate images that fuse the concept with the emotion's content. Table 3 shows this approach is more effective than baselines.

    (Manual transcription of Table 3 from the paper)

    Method	Emo-A ↑		CLIP-img ↑		CLIP-txt ↑
    	amusement	fear	amusement	fear	amusement	fear
    Stable Diffusion	51.54%	56.67%	0.929	0.825	0.257	0.251
    Textual Inversion	60.82%	40.00%	0.902	0.792	0.270	0.259
    Ours	72.16%	63.33%	0.913	0.841	0.276	0.270

  • Emotion Fusion (Figure 8b): By combining the vector representations of different emotions (e.g., amusement + awe), the model can generate images that evoke a complex blend of feelings, showing a fusion of their respective visual concepts.

    该图像是图表，展示了论文中的情绪迁移和情绪融合效果。(a)部分通过添加不同情绪向量（如正面amuse、负面fear）调整同一物体的视觉表现；(b)部分展示了情绪向量叠加后生成的新情绪图像，体现了情绪组合的多样性和丰富性。

• Conclusion Summary: The paper successfully introduces and tackles the novel task of Emotional Image Content Generation (EICG). By creating a dedicated emotion space and a sophisticated mapping mechanism to CLIP space, guided by attribute loss and emotion confidence, the EmoGen model generates images that are semantically diverse, clear, and emotionally faithful, significantly outperforming existing methods.

• Limitations & Future Work: The authors acknowledge two main limitations:

  1. The method focuses exclusively on content, while other visual elements like color and style also contribute to emotion. Future work could integrate these factors for even more powerful emotional generation.
  2. The relationship between content and emotion is treated as somewhat binary via the emotion confidence score. In reality, a concept's emotional association can be complex and context-dependent (e.g., a red rose vs. a white rose).

• Personal Insights & Critique:

  • Strength: The core idea of shifting from style modification to content generation is a significant conceptual leap and is very well-motivated. The use of emotion confidence as a dynamic loss weight is particularly elegant and effective.
  • Contribution: The introduction of the EICG task and its corresponding metrics (Sem-C, Sem-D) is a valuable contribution that provides a framework for future research in this area.
  • Dependency: The method's success is heavily tied to the availability of a large-scale, richly annotated dataset like EmoSet. Its performance might be limited if such detailed attribute labels are not available.
  • Potential for Extension: The framework is highly extensible. One could imagine incorporating user feedback to refine the emotion-to-content mapping, or extending it to video generation. The emotion fusion application, in particular, opens up fascinating possibilities for digital art and creative tools.