CoEmoGen: Towards Semantically-Coherent and Scalable Emotional Image Content Generation
Authors: Kaishen Yuan
Yuting Zhang
Shang Gao
Yijie Zhu
Wenshuo Chen
Yutao Yue
Venue: ICLR 2026 Poster (2026)
Keywords: Emotional Image Content Generation
Semantically-Coherent Sentence-level Guidance
Hierarchical LoRA
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Emotional Image Content Generation (EICG) aims to generate semantically clear and emotionally faithful images based on given emotion categories, with broad application prospects. While recent text-to-image diffusion models excel at generating concrete concepts, they struggle with the complexity of abstract emotions. There have also emerged methods specifically designed for EICG, but they excessively rely on word-level attribute labels for guidance, which suffer from semantic incoherence, ambiguity, and limited scalability. To address these challenges, we propose CoEmoGen, a novel pipeline notable for its semantic coherence and high scalability. Specifically, leveraging multimodal large language models (MLLMs), we construct high-quality captions focused on emotion-triggering content for context-rich semantic guidance. Furthermore, inspired by psychological insights, we design a Hierarchical Low-Rank Adaptation (HiLoRA) module to cohesively model both polarity-shared low-level features and emotion-specific high-level semantics. Extensive experiments demonstrate CoEmoGen’s superiority in emotional faithfulness and semantic coherence from quantitative, qualitative, and user study perspectives. To intuitively showcase scalability, we curate EmoArt, a large-scale dataset of emotionally evocative artistic images, providing endless inspiration for emotion-driven artistic creation. The dataset and code are available at https://github.com/yuankaishen2001/CoEmoGen.

Published as a conference paper at ICLR 2026
COEMOGEN: TOWARDS SEMANTICALLY-COHEEEN AND SCALABLE EMOTIONAL IMAGE CONTENT GEN- ERATION
Kaishen Yuan' Yuting Zhang",, Shang Gao', Yijie Zhu", Wenshuo Chen', Yutao Yuct.3t
The Hong Kong University of Science and Technology (Guangzhou)
2 Harbin Institute of Technology (Shenzhen)
3 Institute of Deep Perception Technology, JITRI
cooneect. hkust-gg..ee edu. (kyuan156, yzhang 3aaaannneoeeecocecceccc yutaoyue edu.cn
ABSTRACT
Emotional Image Content Generation (EICG) aims to generate semantically clear and emotionally faithful images based on given emotion categories, with broad application prospects. While recent text-to-image diffusion models excel at gen- erating concrete concepts, they struggle with the complexity of abstract emotions. There have also emerged methods specifically designed for EICG, but they exces- sively rely on word-level attribute labels for guidance, which suffer from seman- tic incoherence, ambiguity, and limited scalability. To address these challenges, we propose CoEmoGen, novel pipeline notable for its semantic coherence and high scalability. Specifically, leveraging multimodal large language models (MLLMs), we construct high-quality captions focused on emotion- trogtinntriggering con- tent for context-rich semantic guidance. Furthermore, inspired by psychological insights, we design Hierarchical Low-Rank Adaptation (HiLoRA) module to co- hesively model both polarity-sharee low-level features and emotion -ppeiii high- level semantics. Extensive experiments demonstrate CoEmoGen's 's superiority in emotional faithfulness and semantic coherence from quantitative, qualitative, and curate EmoArt,
user study perspectives. To intuitively showcase scalability, a large-scale dataset of emotionally evocative artistic images, providing endless inspiration for emotion-driven artistic creation. The dataset and code are available at
INTRODUCTION
"The artist is a receptacle for emotions that come from all over the place: from the sky, from the earth, from scrap of paper, from passing shape..." -Pablo Picasso
Emotion an innate human instinet that shapes our perceptions and reactions to the world (Minsky. 2007), and to better enable artificial intelligence to understand and respond to human emotional needs, affective computing has rapidly advanced in recent years (Tao & Tan 2005). with Visual Emotion Analysis (VEA) emerging as hot research area. VEA explores the emotional information embedded in visual stimuli and analyzes human responses, offering significant potential for real- world applications such mental health (Wieser et al.] 2012) Li et al. 2024 2025a) and more. As the field progresses, an intriguing question arises: can we reverse the VEA paradigm, shifting from emotional recognition generating images that evoke specific emotions, thereby enabling emotionally intelligent content creation?
Owing the remarkable advancements in diffusion models (Ho & Salimans, 2022 Dhariwal & Nichol 2021 Song et al.) 2020: Ho et al.) 2020). a large number of text-to-image models have emerged, enabling users to input prompts and control conditions to generate high-quality customized
images (Zhang et al. 2023 Peebles & Xie 2023 Ruiz et al. 2023 Rombach et al. 2022 Tian et al.
"Equal contribution Corresponning author

---

Published as a conference paper at ICLR 2026
Amassemen"
Cantent mont
"Paditive
"Pear" 'Diegust
'Aeger"
"Sofnesd"
"Negattvve
Figure 1: Comparison of sentence-level captions and word-level attribute labels as semantic guidance, where the latter suf- fers from (a) lack of contextual associa- tion, (b) weak correlation to emotions, and (c) missing annotations.
MOLL
Figure 2: Emotions of the same polarity exhibit sim- ilarity in low-level features but differ in high-level fine-grained semantics.
2025 Gal et al. 2022). Unfortunately, although these existing models are proficient at generating concrete concepts (e.g., dogs, tables, cars), they struggle and even collapse when tasked with gen- erating abstraci concepts such as emotions (e.g., conteniment, awe, sadness) (Yang et al. 2024). This limitation hampers the progress of emotional intelligence, highlighting the urgent need for the exploration of model capable of generating images that evoke specific emotions.
Some attempts have sought to achieve target emotion conveyance by modifying the color or style of images, but their potential is constrained by fixed image content (Liu et al. 2018 Weng et al. 2023). From a psychological perspective, visual emotions are strongly correlated with specific se- mantics (Brosch et al. 2010), making mere adjustments to low-level features ineffective in veying the intended emotion. To bridge the gap between abstract emotions and concrete im- ages, EmoGen (Yang al. 2024) pioneered the definition of Emotional Image Content Genera- tion (EICG) as generating semantically clear and emotionally faithful visual content based on a given emotion, and explored leveraging word-level attribute labels (i.e., objects or scenes) from EmoSet (Yang et al. (2023) as guidance to align the constructed emotion space with the semanti- cally rich Contrastive Language-Image Pre-training (CLIP) (Radford et al. 202T] space, making a promising step toward effective emotional image generation. Nevertheless, overly relying on at- tribute labels poses the following limitations: (1) Restricted word-level and used in isolation, attribute labels lack contextual connections, which prevents them from conveying comprehensive semantics (Figure T (a)). (2) Some annotated attribute labels may weakly correlate with emotions, while the truly emotion emotion-triggenne triggering elements missing, leading ambiguity (Figure [ (b)). (3) Attribute labels in EmoSet are not always annotated, which significantly limits the diversity and scalability of training corpus (Figure (c)). Therefore, exploring ways overcome these limita- tions and achieve greater flexibility in EICG is a worthwhile direction for research.
Based on the above observations, propose CoEmoGen, novel pipeline towards semantically coherent and highly scalable EICG. Specifically, we focus on sentence-level captions rather than word-level attribute labels for semantically-cohhrees guidance, integrating visual-emotional logic through rich context and achieving alignment closer to human cognition. Leveraging the powerful capabilities of multimodal large language models (MLLMs) (Li et al., 2025c), we organize effec- tive captions that are concise yet focused on emotion-related content for the images in EmoSet, and use CLIP space for filtering to mitigate the noise inevitably introduced by MILMM hallucina- tions (Bai et al.) 2024). Figure [ shows the superiority of sentence-level guidance over werd-level guidance, while the aforementioned standardized construction paradigm also lays the foundation for high scalability. Furthermore, inspired by the psychological observa- tion that emotions the same polarity (positive or negative) share similarities low-level visual attributes (e.g., brightness, color) but differ in high-level semantic features (as illustrated in Fig- ure 2 (Yang et al. 2023), we propose a Hierarchical Low-Rank Adaptation (HiLoRA) module, which includes poharity-shared LoRAs to capture common base features within the same polar-
2

---

Published as a conference paper at ICLR 2026
ity and emotion-specific LoRAs to learn the fine-grained exclusive elements of specific emotions. Extensive experiments, including quantitative comparisons, qualitative analyses, and a user study, demonstrate the superiority of CoEmoGen in semantic coherence and emotional fidelity. To further showcase its scalability, we collect EmoArt, a dataset of 13,633 emotionally evocative artistic im- ages, which CoEmoGen effortlessly leverages inspire artists in creating emotion-driven artworks. Our contributions can be summarized as follows:
We propose CoEmoGen, novel semantically-cohereen and highly scalable EICG pipeline.
We introduce reliable captions with emotion-related content for EmoSet, leveraging their rich coherent context for sentence-level semantic guidance.
Inspired by psychology, we design a HiLoRA module with polarity-shared LoRAs for mod- eling the common base features and emotion-specific LoRAs for capturing the fin-graaned exclusive elements.
Extensive experiments, encompassing quantitative, qualitative, and user study, showcase CoRmoGen's superiority in EICG, while EmoArt, a dataset composed of emotionally stim- ulating artworks, is constructed further highlight its flexible scalability.
2 RELATED WORK
Visual emotion analysis. Visual Emotion Analysis (VEA) aims to predict people's emotional re- sponses to visual stimuli and has been studied for over two decades (Zhao et al. 2021 Mohamed al., 2022 Achlioptas et al., 202T). The most commonly employed Mikels model classifies emo- tions into eight categories: anusement, awe, contentment, excitement, anger, disgust, fear, and sad- ness, with the first four belonging to the positive polarity and the latter four to the negative polar- ity (Mikels et al.] 2005). In the early stages, inspired by psychology and art theory, Machajdik & Hanbury (2010) extracted specific image features related to emotions, such as color and composi- tion, for analysis. With the rapid development of deep learning. |ao et al. (2020) proposed MIdrNet, which integrates different levels of deep representations (image semantics, aesthetics, and low-level features) to classify emotions across various types of images. YYang al. (2021) first introduced stimulus selection, extracting unique emotional features from different stimuli. Ultimately, VEA is a classification task, while reversing this process to generate images that evoke specific emotions has broad practical applications in areas such as mental health and artistic creation, making it worth further exploration.
Emotional image content generation. Recently, with the remarkable progress of diffusion mod- els (Ho & Salimans| 2022]] Ho et al., 2220),, large number of text-to-image models have emerged, allowing users to generate high-quality and diverse images (Peebles & Xie, 2023 Betker et al.) 2023 Rombach et al. 2022, Tian et al., (025). Furthermore, to achieve customization, several personalized text-to-image generation methods (Zhang et al.) 2023 Ruiz et al.] 2023 Zhu et al.) 2024] have been proposed, which typically rely on learning new embeddings or fine-tuning model parameters. While these methods have attained impressive results in generating concrete concepts, they fall short when it comes to abstract concepts such as emotions. Some attempts aim to modify low-level visual elements to alter the emotional tone of input image, thus performing image emotion transfer. Representative works include Peng el al. 's changing the emotions evoked by adjusting the image's color and texture features (Peng et al., 2015), and Sun et al. 's proposal activate a speci- fied emotion through style transfer (Sun et al. 2023). Due to the fixed content of the image, and the strong correlation between visual emotions and specific semantics, simply modifying these super- ficial features is insufficient to evoke the desired emotion. Therefore, EmoGen (Yang et al. (0244) initially defined the EICG task as creating semantically clear and emotionally faithful visual content based on specified emotion category, and it achieved promising outcomes in generating emotion- ally evocative images. Nevertheless, due to the reliance on word-level attribute labels, which lack semantic context, are weakly related to emotions, and often suffer from label deficiencies, EmoGen lacks semantic coherence and flexibility in practical applications. Unlike EmoGen, the proposed CoEmoGen introduces sentence-level captions supervision and designs a HiLoRA module in ac- cordance with psychological consensus, resulting in a samantically-coheree and highly scalable EICG pipeline that excels in both semantic clarity and emotional fidelity.
3

---

Published as a conference paper at ICLR 2026
L Prongt wits prire
Traliin
cerpai
Disorring
(a) Data construction procedure and examples of filtering
(b) Emotion distribution (c) Wordcloud
Figure 3: Summary of sentence-level coherent semantic guidance acquisition
METHODOLOGY
3.1 COHERENT SEMANTIC ACQUISITION
EmoSet (ang et al. 2023) is a recently constructed large-scale visual emotion dataset that follows the popular caghooyy Mikels model. It retrieves relevant images from the Internet based on emotions and their synonyms, annotating 118, 102 images not only with emotional labels but also with reeattd visual attributes different levels, including low-level (i.e., brightness, col- orfulness), mid-level (i.e,, scene type, object class), and high-level (i.e. facial expressions, human actions). The annotation process is semi-automated; for example, scene types are annotated using scene recognition model trained on Places365 dataset (Zhou et al. (017). while object classes are annotated using an object detection model trained on Openlmages V4 dataset (Kuznetsova et al. 2020). Despite manual intervention for corrections, these word-level labels still suffer from lack of coherent associations, emotional ambiguity, and missing elements. As a result, EmoGie (Yang al. 2024),, which relies attribute labels such scenes objects for semantic guidance, faces challenges in terms of semantic coherence and the scalability of training corpus.
Recognizing the shortcomings of attribute label supervision, coherent sentence-level captions as guidance. Thanks to the remarkable capabilities demonstrated by recent MLLMs (Li et al. 2025c b), generating a tailored caption for an image has become cost- effective and feasible. Based on this, we meticulously design prompt that takes the given im- age's corresponding emotion label as prior knowledge while enforcing a focus on different levels of emotion-related visual attributes, aligning with those mentioned in EmoSet, thereby encouraging MLLMs to produce concise yet emotion-rich caption. The designed prompt is as follows:
seek to introduce context-rich and
<Image> This image evokes a strong emotion of <omot ion>. Provide a one-sentence caption that vividly describes the visual details, focusing on elements like brightness. colorfulness, scene type, object classes, facial expressions, and human actions that effectively convey and express this emotion.
We investigate the impact of captions obtained from different prompts on subsequent emotional image generation, with details provided in Section 4.3 We equip all images in EmoSet with corre- sponding captions. However, is worth noting that due the inherent hallucinations MILMs, which refer to phenomenon where models generate unrealistic fabricated content (Bai et al.) 2024 Xing et al., 2025), some inaccurate captions are inevitably produced. To address this, we compute the similarity of the preliminarily obtained cet al., 2021). discarding the bottom 20% of samples in each category based on similarity rankings ensure sufficiently reliable semantic guidance. The standardized data construction procedure sets the stage for high scalability, and this entire process, along with examples of filtering, shown in Figure (a). The emotional distribution of the final training corpus presented in Figure 3 (b),
pairs in the CLIP space (Radford)
while a word cloud for each emotion, based on corresponding captions, is shown in Figure 3 (c),
with clearer ones provided in Appendix.
3.2 COEMOGEN
Based the aforementioned constructed dataset D, propose CoEmoGen, EICG pipeline developed for semantic coherence and high scalability.
Vs. c.), where I, represents the i-th image, yi denotes the cor- responding emotion label, and c; indicates the generated caption, we first apply one-hot encoding to y: setting the position of the present category to 1 and all others O, obtaining yf. The input y!
Overview. Given sample D, (1,
4

---

Published as a conference paper at ICLR 2026
"A large wrfitabll Senta cloup stainding front of house, holding blue ond red gift bog.
B
Text Encodor
Visual-penceptin Encoder
Arger
U-Net
Ai
Bi
Neuro-symbole Mapper
Image Encoder
Sturtes perele
Bi
[1.00,000000]
"Amusement"
Diffudion Process
Traiable
Hepelin Polenty Lnd
Fruzon
Hanarchica (-ILBA))
Figure 4: The overall architecture of the proposed CoEmoGen. The one-hot vectors derived from emotion categories first undergo neuro- neuro-symbolic mapping and interact with the visual embedding, then are encoded by the encoder obtain emotion conditions. which further guide the denoising network U-Net equipped with Li,,,,, which consists of emotion-specifie LoRAs and polarity- shared LoRAs
is then processed by a Neuro-symbolic Mapper composed of fully connected layers with non-linear activations obtain the emotional descriptor e;. This neuro-symbolic mapping provides greater flexibility and diversity in emotional image generation, which is further discussed in Section 4.3 To achieve more precise alignment with contextually coherent and semantically rich captions, en- hancing perception and interaction with visual information is beneficial. Specifically, we fuse the neuro-symbolic vector e, with the visual embedding vi, which is obtained by encoding I through the CLIP image encoder, utilizing Pesual-Perceotion Encoder primarily based cross-attentis mechanism. This process can be formulated as follows:
Softmax es W (w. Ju,
(1)
Vdg
where ef is visually-enhanced emotion descriptor, W WA, and W, are learnable weights, and do feature dimension. Subsequently, e fed into CLIP text encoder obtain the emotion condition ef. which further contributes to the U-Net equipped with Hierarchical LoRA. The entire process described above is illustrated on the left side of Figure 4
Hierarchical LoRA. Low-Rank Adaptation (LoRA) (Hu et al., 2022) is popular parameter- efficient fine-tuning technique that trains only a small number parameters through low-rank matrix decomposition, enabling the fine-tuning of pre- rre-trained model weights at lower computational cost. Its core concept can be formulated as follows:
W W + AW
A. B, = A
(2)
where W and W' E ddd represent the weights before and after adaptation, respectively, with A E Raxr and B E Brxd' being the introduced low-rank adaptation matrices, satisfying the condition r & min(d.k).
However, in performing EICG, when dealing with the complex concept of representing emotions, a single LoRA proves insufficient. Inspired by psychological observations that emotions with the same polarity share common characteristics in low-level features such as brightness while differing in high-level semantic elements (as shown in Figure 2). we design a hierarchical LoRA (HiLoRA) module, which consists of eight emotion-specific LoRAs (AWe) === tailored each emotion and J)=1 two polarity-shared LoRAs (AW?)?= These LoRAs function with distinct roles, with only the LoRAs corresponding to the input image's 's emotion label and its associated polarity activated during updates. Taking amusement (j = 2) under the positive polarity (k 1) as an example, Equation transforms into:
-' W AWT + AWS = W AP BT A5 B5.
(3)
Here, AWP is responsible for modeling the high brightness and colorfulness associated with the positive polarity, while AW5 captures the high-level fine-gre grained semantics related to amusement. The detailed structure of HiLoRAA is shown the right side of Figure
5

---

Published as a conference paper at ICLR 2026
Loss function. During training, optimize the Neuro-symbolic Mapper, coder, and HiLoRA while keeping the image encoder, text encoder, and U-Net frozen. Our loss constraints consist of two components. One is the Latent Diffusion Model (LDM) loss, which guides the learning of pixel-level representations:
En-
LLDM (g [lle (,)),,
where {-) denotes the latent encoder, <g(-) indicates the denoising network U-Net, e refers to the added noise, and 2; the latent noise at time step f. However, applying LLDM alone may lead to an excessive focus pixel-level commonalities and even collapse into specific semantics. To maintain the semantic diversity and coherence of the same emotion, we explicitly approximate the cosine similarity between the emotion condition ef and the encoded sentence-level caption in the CLIP space, introducing the semantic loss LSEM, formulated as follows:
SSEM = 1 eg T(G))
(5)
[e)]]]](<)]]]
where T the CLIP text encoder. The combination of the above two losses achieves synergy between pixel-level guidance and sentence-level coherent semantic guidance.
During inference, similar to Yang et al. (2024), we obtain visual embedding vs by randomly sam- pling from corresponding emotion cluster, which is pre-constructed and represented by a Gaussian distribution, achieving a high degree diversity.
EXPERIMENTS
4.1 SETTINGS
Implementation details. We initialize the latent encoder and denoising network with pre-trained weights from Stable Diffusion v1.5 (Rombach et al., 2022) and choose the pre-trained CLIP ViT- L/14 (Radford et al. 2021) model for both the image and text encoders to ensure fair comparison. Regarding the model configuration, the hidden layer size of the Neuro-symbolic Mapper is set to 512, the embedding dimension dg in the Visual perceppprrepttion Encoder is set to 768, and the rank in HiLoRA is set to 4. During training, we set the batch size to 1, utilize the Adam optimizer with of 1 for 130,000
81 0.9, 82 0.999, weight decay of 1 and constant learning iterations. Following Yang et al. (2024), we also adopt a random oversampling strategy to help mitigate class imbalance. All experiments are conducted on two NVIDIA RTX 4090 GPUs with PyTorch. Metrics. To comprehensively evaluate model's performance in executing the EICG task in terms of emotion fidelity, semantic clarity, and semantic diversity, following Yang et al. (0244), we generate 1, 1,000 images for each emotion and employ five evaluation metrics: (1) FID (Heusel al., (017)) to measure the distribution distance between generated and real images to assess fi- delity, (2) LPIPS (Zhang et al.] (2018) to assess the overall diversity of the generated images, (3) Emo-A (Emotion Aceuracy) based on emotion classification accuracy to evaluate the consistency between the generated images and the target emotion. (4) Sem-C (Semantic Clarity) to measure the explicitness of the generated image contents, and (5) Sem-D (Semantic Diversity) to quantify the richness of content corresponding to each emotion in the generated images. The implementation details of these metrics are in the Appendix.
4.2 COMPARISONS
We compare the proposed CoEmoGen with the most relevant and state-of-the-art generation mod- els. including the general image generation model Stable Diffusion, the customized image gen- eration models Textual Inversion and Dreambooth, as well as EmoGen, which specifically de- signed for EICG. The fine-tuning of all the above comparison methods is consistent with that in EmoGen. Qualitative analysis. Figure 5 presents a qualitative comparison between our proposed CoEmoGen and other methods, with analysis focused on three emotional categories: awe, anger, and contentment (complete results for all emotions are provided in Appendix). It can be observed that both general and customized image generation models struggle to capture the complex and diverse semantic elements of a specific emotion, often collapsing into a single feature point or exhibit- ing severe semantic distortion, which makes them highly uncontrollable. In comparison, EmoGen

---

Published as a conference paper at ICLR 2026
Aax'
AAner
"ontenimmnn"
gtade
Teanel
Drandeoth
Enuden
nlo Euno
CM LKTI
WiS cntioo
CrEreden (Ourd
Figure 5: Qualitative comparisons of the proposed CoEno with state-of- sfate-of-thh-ae ohetre-rrtttttt generation meth- ods and ablation variants.
FID
Esx-AA
ScarC1
Tonnal Imargon
90.51
11310
28.775
0.055
Bodt,
45.99
0.661
70.50%
Dd14
DO1TS
EraaCle Yaagari 2015
4.000
0.17
76.56
D633
L.RAs
15:30
01315
1755
D635
[USTER
we plankyylaaa LiRto
4.47
0.724
78,39
0638
0.0366
Table 1: Quantitative comparisons with state-of-the- art methods and ablation variants on the EICG task, covering five metrics.
Figure 6: User study results on preference selection between CoEmoGen and com- parative method.
introduces attribute labels as semantic guidance, improving diversity and emotional conveyance, whereas, constrained by the word-level supervision, it unintentionally binds emotions to specific objects scenes, leading to unnatural collage-like combinations that challenge semantic coherence. For example, anger is frequently associated with fire, tigers, and wide-open mouths, and although EmoGen indeed successfully captures these elements, fails to consider their semantic relation- ships, resulting in unrealistic outputs such as a flaming tiger. contrast, the proposed CoEmoGen achieves more natural and vivid emotion conveyance, owing to context-rich sentence-level captions that constrain logical connections between elements as guidance, which ensures semantic coherence. Moreover, CoEmoGen preserves diversity while generating images with richer colors and smoother visuals, drawing from the cooperation of emotion-specific LoRAs for fine-grained semantics capture and polarity-sharee LoRAs for low-level feature modeling HiLoRA. Another noteworthy aspect that CoEmoGen demonstrates versatile photographic composition, rooted in camera-related words like "closeeup" in the captions, giving the generated images stronger sense of perspective and realism. Quantitative comparison. Table quantitativell compares CoEmoGen with other meth- ods, demonstrating its overall performance advantage by leading across all five evaluation metrics. Specifically, Emo-A shows an improvement of at least 3.9%, confirming the accuracy of emotional conveyance the generated images. FID indicates that the generated images better align with the real data distribution, while Sem-C ensures consistent semantic clarity throughout the generation process. Additionally, LPIPS and Sem-D highlight the dual advantages of visual diversity and se- mantic richness in the generated results. These quantitative findings align with the visualizations in
7

---

Published as a conference paper at ICLR 2026
Figure jointly proving CoEmoGen's 's superiority in achieving the EICG task. User study. To eval- uate whether CoEmoGen's 's generation aligns with human perceptual cogmition, we further conduct comprehensive user study in addition to the aforementioned qualitative and quantitative analyses. The user study involved 17 participants (8 female and 9 male) aged 13-51 with diverse backgrounds. Each participant was presented with two image groups conveying the same target emotions, one generated by CoEmoGen and the other by comparative method, and asked to select their pref- erence through four criteria-oriented questions: (1) "Which group appears more realistic?" (Image fidelity); (2) "Which group better evokes [specific emotion]?" (Emotion fairhfulness); (3) "Which group shows greater diversity?" (Semantic diversity); (4) "Which group demonstrates more natural coherence?" (Semantic coherence). As evidenced in Figure [ CoEmoGen achieves overwhelming preference across all four dimensions, particularly excelling in semantic coherence with an average selection rate of 88.42%. Notably, despite being relatively close to EmoGen in some quantitative metrics, CoEmoGen secures an average user preference rate of 78.67% in direct pairwise compar- isons, demonstrating superior alignment with human emotional perception and cognitive intuition, which ultimately translates into stronger emotional resonance in practical applications.
4.3 ABLATION STUDY
We conduct ablation studies to explore the effects of each component of the proposed CoHmoGen, as well as the selection of important settings.
We individually remove each component from the complete CoEmoGen and observe the results to explore its effect, which is quantitatively presented in Table [ and qualitatively shown in Fig- ure 5 Semantic loss. It can be observed that when LSEM is absent, the model, deprived of the essential semantic guidance and relying solely on LLDM, excessively focuses on pixel-level recon- struction, even collapsing into specific semantics, leading to significant semantic distortion and a lack of semantic diversity. Emotion-specific LoRAs. When emotion-specine I LoRAs are removed, the model's ability to capture emotion-specific fine-grained semantics diminishes. Meanwhile, the effect of polarity-shared LoRAs introduces semantic entanglement among emotions belonging to the same polarity. As a result, although the model performs reasonably well in terms of seman- tic clarity and diversity, the semantic confusion among emotions of the same polarity significantly reduces the accuracy of conveying the target emotion and amplifies the divergence from the true distribution. Polarity-shared LoRAs. In comparison, when polarity-shared LoRAs removed, the quantitative results appear to show only a slight decline compared to the complete CoEmoGen. However, qualitative comparisons reveal that the color saturation and brightness tend to become overly monotonous or unnatural, which further confirms the important role of polarity-sharee Lo- RAs in modeling the low-level features of polarity. In summary, each component of CoEmoGen performs its specific function, and when working in unison, they exhibit the optimal performance in terms of emotional fidelity, semantic diversity and coherence.
Table 2 compares the results of different choices of important settings. Type of caption. To ob- tain the most suitable captions as sentence-level semantic guidance, we explore four prompts to
Recciiua Price
Dealls
Fm]
1PBS5
Fato-tt
Sen-Ch
Ser-D"
0.114
0716
31/05
00015
43.58
07M
77.47%
0.678
0.0366
(a) Type of captions for semantic guidance.
drive the MLLMs, controlling whether the cap-
tions focus on emotion-related elements and con-
Type of Input
FID
LPIPS1
Emo At
Sem
Sem D
tain complete details, with further details pro-
Leimable
Text
43.77
42:38
0.705
0.697
78.49%
78.9%%
0.611
0.0275
vided in the Appendix. As shown in Table 2 (a),
Ose-box
40066
0.732
80.15%
0.641
0.0349
adding emotional priors to the prompt promotes consistent improvement in the model's ability, highlighting the importance of focusing on emo- tional elements. However, removing the restric- tion to generate only one sentence and allow- ing the inclusion of as many details as possible leads to performance degradation, likely due to the noise introduced by irrelevant elements and the increased risk of hallucinations in MLLMS with longer captions, making model convergence more difficult. Therefore, we ultimately choose prompt with emotional priors and the constraint of generating single sentence. Type of input. We compare three different types of input representa-
(b) Type of inputs for emotion representation.
Type of
FIDJ
LPPI55
Ems-At
Scn-C1
Son-D>
MAB
42.40
0.721
78,87%
0.626
00311
MSE
41:57
0.725
79.04F%
0.618
B0927
K-1
48.36
0.693
20.01%
0.609
0,2222
Cosine
40.66
0,733
80.15%
0.641
003349
(c) Type of semantic constraint mechanisms LSEM.
Table 2: Quantitative results of CoEmoGen with different choices of important settings. Default settings are marked in gray
8

---

Published as a conference paper at ICLR 2026
tions, including learnable class vectors, frozen text encodings of emotion class names, and the one- hot vectors with the Neuro-symbolic Mapper. As shown in 2 (b), experimental results reveal significant limitations in the first two types in terms of diversity, which is due to the representation bottleneck from the limited capacity of learnable vectors and over-constraining semantics in the em- bedding space from the frozen text encoder. In contrast, the Neuro-symbolic Mapper expands the latent space exploration while maintaining semantic consistency through a differentiable symbolic reasoning mechanism, enabling greater diversity. Design of CSEM- In the design of the semantic loss LSEM, constructing an effective semantic constraint mechanism is question worth considering. To address this, we systematically compare the performance differences of four constraint meth- ods: Mean Absolute Error (MAE), Mean Squared Error (MSE), Kullback-Leibler Divergence (K-L divergence), and Cosine Similarity. As shown Table 2 (c), Cosine Similarity achieves the best performance, owing to its inherent advantage in measuring semantic similarity in high-dimensional vector spaces, enabling more precise guidance for the model to capture deep semantic relationships. Therefore, choose it by default.
4.4 SCALABILITY OF COEMOGEN
Thanks to MILMS facilitating the accessibility of sentence-level semantic guidance captions, Co- EmoGen exhibits high scalability compared to EmoGen, which relies on high acquisition costs and limited word-level attribute labels for supervision. This feature enables CoEmoGen incorporate any emotionally evocative images into the training corpus.
To illustrate this, we construct a large- scale emotional art image dataset, EmoArt. Specifically, we first collect approximately 100,000 artistic images from WikiArt, cov- ering 129 artists, 11 genres, and 27 styles. Then, we utilize classifier pre-trained on EmoSet to predict the emotional cat- egories of these artistic images, setting an emotion confidence threshold of 0.75, which ultimately filters out 13,633 strongly emotionnrrpprssentaiie samples. Notably, due to the low-frequency presence of ex- citement and disgust in artistic expressions (accounting for less than 1%) (Pearce et al.) 2016). EmoArt focuses on the remaining six emotion categories. EmoArt's images across different emotions is provided Appendix. Next, following the same process Figure (a), obtain reliable sentence-Ieve captions for each image, which are then utilized to train CoEmoGen. Fig- ure presents the results generated by EmoArt-driven CoEmoGen, demonstrating its ability to cre- ate emotionally faithful and stylistically diverse artistic images for each emotion category, offering endless inspiration for artists in crafting emotionally evocative artworks.
AAuutemenn
Asye'
"CCnntettmnn"
"Feort
Annger
"odnees
visualization of
Figure 7: Visualization of emotionally evocative artis- tic images generated by AmoArttdriven CoEmoGen for each emotion.
The construction of EmoArt intuitively showcases CoEmoGen's high flexibility and scalability, allowing users to follow a standardized construction paradigm effortlessly customize diverse emotion-rich datasets and generate emotionally stimulating images tailored to their specific needs.
4.5 APPLICATIONS
Given its strong emotional content generation capability, we further explore CoEmoGen's ' intrigu- ing applications. Emotion Transfer. By integrating the emotional representations learned by Co- EmoGen with common neutral elements, we achieve targeted emotion transfer, as shown in Figure Excitingly, this results in a seamless fusion, where the emotional semantics are incorporated while staying true to the original neutral elements. Importantly, instead of rigidly overlaying emotion- related elements onto the neutral ones, this process perceives the semantic characteristics of the neutral elements and adaptively adjusts their textures, colors, and layouts to ensure overall coher-

---

Published as a conference paper at ICLR 2026
"Bog"
"Book'
"Cake"
"Cor'
"Tree"
"House"
"Street"
"Bridge"
"None"
"Anusement"
13:05
Feear
Figure 8: Visualization of emotion transfer by fusing emotion representations with neutral elements.
"Amusement
'mmusement
"Awe
Figure 9: Visualization of emotion fusion by combining different emotion representations.
ence. For example, in the fear transfer applied to street scene, CoEmoGen enhances shadow contrast and spatial depth, generating a dark and deep corridor that aligns with real-world lighting principles rather than mechanically adding horror symbols, ensuring a gradual and immersive in- fusion of emotional semanties and offering promising potential for interpretable image emotional editing. Emotion Fusion. We also explore the possibility of combining different emotional repre- sentations, as shown Figure 9 which illustrates the pairwise combinations of amusement, awe, and fear, showcasing visually rich narrative intertwined with complex emotions. For example, in amusement+awe, colorful balloons and a rainbow in mountain valley create a fantastical yet digni- fied visual language, while in awe+fear, the transformation of mountain contours shapes the rock formations into a devil-like face, collectively reinforcing sense of wonder and fear. When fusing emotions, are essentially organically integrating emotional semantics at visual level, allowing CoEmoGen not only to generate single-emotion images but also to explore more multi-dimensional emotional experiences.
CONCLUSION AND FUTURE WORK
In this paper, develop CoEmoGen, novel EICG pipeline excelling in semantic coherence and high scalability. Leveraging MLLMs, we obtain reliable context-rich sentence-level captions for the images EmoSet to serve as semantically-cohhe
guidance. Referring psychological the- ory, we design HiLoRA module, including polarity-shared LoRAs to model common low-level features and emotion-specific LoRAs to capture high-level exclusive semantics. We demonstrate the superiority of the proposed CoEmoGien in EICG from qualitative, quantitative, and user study perspectives, and further collect and construct a large-scale emotional art image dataset, EmoArt, to concretely showcase high scalability. In future work, plan to investigate the denoising process in depth, explicitly analyzing the dynamic shifts of attention toward emotion-related attributes across different denoising stages.
ACKNOWLEDGGENNT
This work supported by the Guangzhou-HKUSTGGG Joint Funding Program (Grant No. 2023A03J0008), Education Bureau of Guangzhou Municipality. It was also supported by Jiangsu Industrial Technology Research Institute (IITRI) and Wuxi National High-Tech District (WND).
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov, Mohamed Elhoseiny, and Leonidas J Guibas. Artemis: Affective language for visual art. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 11569-11579, 2021.
Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He, Zongbo Han, Zheng Zhang, and Mike Zheng Shou. Hallucination of multimodal large language models: A survey. arXiv preprint arXiv:2404. 18930, 2024.
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-. pdf, 2(3):8, 2023.
Tobias Brosch, Gilles Pourtois, and David Sander. The perception and categorisation of emotional stimuli: A review. Cognition and emotion, 24(3)3377-400, 2010.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hi- erarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp. 248-248-2 Ieee, 2009.
Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-8798 2021.
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion, arXiv preprint arXiv:2208. 01618, 2022.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp- 770-778, 2016.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by two time-scale update rule converge to local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Jonathan Ho and Tim Salimans. arXiv:2207. 12598, 2022.
Classifier-free
diffusion guidance.
arXiv preprint
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang,
Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022.
Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, and Sanjiv Kumar. Rethinking fid: Towards a better evaluation metric for image generation. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9307- 9315, 2024.
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Sha- hab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al. The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale, 2020.
ternational journal of computer vision,
Deng Li, Bohao Xing, and Xin Liu, Enhancing micro gesture recognition for emotion understanding via context-awart visual-text contrastive learning. IEEE Signal Processing Letters, 31:1309- 1313, 2024.
Deng Li, Jun Shao, Bohao Xing, Rong Gao, Bihan Wen, Heikki Kälviäinen, and Xin Liu. Msf- mamba: Motion-aware state fusion mamba for efficient micro-gesture recognition. arXiv preprint arXiv:2510. 10478, 2025a.
11

---

Published as a conference paper at ICLR 2026
Deng Li, Bohao Xing, Xin Liu, Baiqiang Xia, Bihan Wen, and Heikki Kälviäinen. Deemo: De- identity multimodal emotion recognition and reasoning. In Proceedings of the 33rd ACM Inter- national Conference Multimedia, pp. 5707-5716, 2025b.
Yifan Li, Zhixin Lai, Wentao Bao, Zhen Tan, Anh Dao, Kewei Sui, Jiayi Shen, Dong Liu, Huan Liu, and Yu Kong. Visual large language models for generalized and specialized applications. arXiv preprint arXiv:2501.022 022665, 2025c.
Da Liu, Yaxi Jiang, Min Pei, and Shiguang Liu. Emotional image color transfer via deep learning. Pattern Recognition Letters, 110:16-21 22,622222222 2018.
Jana Machajdik and Allan Hanbury. Affective image classification using features inspired by psy- chology and art theory. In Proceedings of the 18th ACM international conference on Multimedia, pp. 83-92, 2010.
Joseph A Mikels, Barbura Fredrickson, Gregory R Larkin, Casey M Lindberg, Sam Maglio, and Patricia A Reuter-Lorenz. Emotional category data images from the international affective picture system. Behavior research methods, 37:626-66 630, 2005.
Marvin Minsky. The emotion machine: Commonsenss thinking, artificial intelligence, and the future of the human mind. Simon and Schuster, 2007.
Youssef Mohamed, Faizan Farooq Khan, Kilichbek Haydarov, and Mohamed Elhoseiny. It is okay to not be okay: Overcoming emotional bias affective image captioning by contrastive data col- lection. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 21263-21272, 2022.
Marcus T Pearce, Dahlia W Zaidel, Oshin Vartanian, Martin Skov, Helmut Leder, Anjan Chatter- jee, and Marcos Nadal. Neuroacsthetics: The cognitive neuroscience of aesthetic experience. Perspectives on psychological science, 11(2):265 279, 2016.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference computer vision, pp. 4195 4205, 2023.
Kuan-Chuan Peng. Tsuhan Chen, Amir Sadovnik, and Andrew C Gallagher. A mixed bag emo- tions: Model, predict, and transfer emotion distributions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 860-868, 2015.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference machine learning, pp- PmLR, 2021.
Tianrong Rao, Xiaoxu Li, and Min Xu. Learning multi-level deep representations for image emotion classification. Neural processing letters, 51:2043- 2061, 2020.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- on computer vision and pattern recognition, pp. 10684-10000 10695, 2022.
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning eoxt-to-image diffusion models for subject-driven generation. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 22500- 22510, 2023.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.025000 2020.
Shikun Sun, Jia Jia, Haozhe Wu, Zijie Ye, and Junliang Xing. Msnet: A deep architecture using multi-sentiment semanties for sentiment-awar image style transfer. In ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 1-5, IEEE, 2023.
12

---

Published as a conference paper at ICLR 2026
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Du- mitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 1-9, 2015.
Jianhua Tao and Tieniu Tan. Affective computing: review. In International Conference on Affec- live computing and intelligent interaction, pp. 981-995. Springer, 2005.
Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. Advances in neural information processing systems, 77:84839-84866 2025.
Shuchen Weng, Peixuan Zhang, Zheng Chang, Xinlong Wang, Si Li, and Boxin Shi. Affective image filter: Reflecting emotions from text to images. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 10810-10819, 2023.
Matthias Wieser, Elisabeth Klupp. Peter Weyers, Paul Pauli, David Weise, Daniel Zeller, Joseph Classen, and Andreas Mühlberger. Reduced early visual emotion discrimination as an index of diminished emotion processing in parkinson's 's disease?-evidene from event-related brain poten- tials. Cortex, 48(9):1207-1217, 2012.
Bohao Xing. Xin Liu, Guoying Zhao, Chengyu Liu, Xiaolan Fu, and Heikki Kälviäinen. Emotion- hallucer: Evaluating emotion hallucinations in multimodal large language models. arXiv preprint arXiv:2505. 1405, 2025.
Jingyuan Yang, Jie Li, Xiumei Wang, Yuxuan Ding, and Xinbo Gao. Stimuli-aware visual emotion analysis. IEEE Transactions on Image Processing, 30:7432-7445, 2021.
Jingyuan Yang, Qirui Huang, Tingting Ding, Dani Lischinski, Danny Cohen-Or, and Hui Huang. Emoset: A large-scale visual emotion dataset with rich attributes. In Proceedings of the IEFF/CVF International Conference on Computer Vision, pp. 20383-20394, 2023.
Jingyuan Yang, Jiawei Feng, and Hui Huang. Emogen: Emotional image content generation with text-to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6358-65886 -3668, 2024.
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference computer vision, pp. 3836-3847 3847, 2023.
Richard Zhang, Phillip Isola, Alexei Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as perceptual metric. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 386-595,40 2018.
Sicheng Zhao, Xingxu Yao, Jufeng Yang, Guoli Jia, Guiguang Ding, Tat-Seng Chua, Bjoern W Schuller, and Kurt Keutzer. Affective image content analysis: Two decades review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6729- 6751,2021.
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. Places: A 10 million image database for scene recognition. IEEE transactions pattern analysis and machine intelligence, 40(6)):452-14444 2017.
Chenyang Zhu, Kai Li, Yue Ma, Chunming He, and Xiu Li. Multibooth: Towards generating all your concepts in image from text. arXiv preprint 1..i::2444.142233 2024.
13

---

Published as a conference paper at ICLR 2026
APPENDIX
A ADDITIONAL VISUALIZATIONS
We present qualitative comparisons of the proposed CoEmoGen with state-of-the-art generation methods and ablation variants across all emotion categories in Figure T0 to 13] from which conclu- sions aligned with those discussed Section 4.2 of the main manuscript can be drawn, demonstrat- ing the consistency. To better illustrate the newly collected large-scale emotional art image dataset, EmoArt, we also provide stylistically diverse sample examples for each emotion category Fig- ure 14 Besides, generate word clouds for each emotion category based the corresponding captions, allowing us to observe the high-frequency words or phrases in each category, as shown in Figure 13
B PROMPTS
To obtain the most suitable and effective sentence-level captions as coherent semantic guidance, we conduct an ablation study in Section 4.3 of the main manuscript, exploring the impact of captions derived from different versions of prompts. The key differences in these prompts lie in two aspects: whether they include emotional priors and whether they restrict the generation to single sentence. The specific details of the adopted prompts provided in Table 3 Additionally, considering the differences in emotional representation elements between artistic and natural images, we design customized prompt specifically for generating captions for artworks in EmoArt, which guides MLLMs to focus on key elements that convey emotions in artistic images, as detailed in Table
Imago>> Provide one-sentence caption that vividly describes the visual details of this image."
"<Image> This image evokes a strong emotion of <emoti ion>. Provide one-sentence caption that vividly describes the visual details, focusing on elements like brightness, colorfulness, scene type, object classes, facial expressions, and human actions that effectively convey and express this emotion."
<mage>> Write
but informative summary of the picture."
"c <Image>> This image evokes strong emotion of <emot ion>. Provide terse but informative summary that vividly describes the visual details, focusing on elements like brightness, colorful- ness, scene type, object classes, facial expressions, and human actions that effectively convey and express this emotion."
Table 3: Different versions of prompts used drive MLLMs for generating semantically coherent sentence-level captions.
<Image> This artwork evokes strong emotion of <emotion ion?. Provide a one-sentence caption that vividly describes the visual details, focusing on elements like artistic style, material textures, composition balance, symbolic elements, dominant color choices, expressive brushstrokes, and dra- matic light/shadow contrasts that effectively convey and express this emotion.
Table 4: A customized prompt specifically designed for generating captions for artworks in EmoArt.
C EVALUATION METRICS
To comprehensively evaluate the model's performance in executing the EICG task, employ five evaluation metrics, which are introduced below.
FID (Fréchet Inception Distance) (Heusel al. 2017). FID is metric employed to evaluate the performance of generative models by measuring the distribution similarity between generated and real images in feature space, which can be formulated as:
FID = ||, 22ll2 + Tr E, + E, - 21E,E,))))2)
(6)
14

---

Published as a conference paper at ICLR 2026
where H4, and Mg represent the means of features extracted from real and generated images using the Inception network (Szegedy et al. 2015), respectively, while Σ, and Eg denote the corresponding covariance matrices. lower FID value indicates closer match between the statistical properties of the generated and real images. is important to note that due to the inherent limitations of FII (Jayasumana et al., 2024), need incorporate other metrics for more accurate evaluation.
LPIPS (Learned Perceptual Image Patch Similarity) (Zhang et al. 2018). We employ LPIPS, a perceptual metric aligning with human vision by measuring image difterences through deep feature correlations, to evaluate the overall diversity of generated images. Specifically, we randomly select P. pairs of generated images for each emotion category and calculate the LPIPS score by averag- ing the perceptual distances between these pairs. Finally, the overall LPIPS metric is obtained by averaging the L.IPS scores across all emotion categories, as formalized below:
C
P
LPIPS = C.P. 1=1
LPIPS (a), 62),
where C' denotes the total number of emotion categories, P. represents the number of sampled image pairs for the i-th emotion, and LPIPSS(a), by,) indicates the perceptual similarity score between the p-th pair of images and b the i-th emotion category.
Emo-A (Emotion Accuracy). Emo-A introduced as metric to quantitatively evaluate emotion faithfulness. It leverages pre-trained emotion classifier to predict the emotion category of gener- ated images, which are then compared against the target emotion. The final accuracy computed exclusively based samples with correct emotion alignment.
Sem-C (Semantic Clarity). To quantify semantic clarity, Sem-C employs a pre-trained object clas- sifier (He et al. 2016) from ImageNet (Deng et al. 2009) and pre-trained scene classifier (He iet al. 2016) from Places365 (Zhou et al. 2017) to classify the generated images. The final metric is computed by taking the highest probability between these two classifiers, formalized below:
1
Sem-C
max (Uobjoc( Usccne (In))
(8)
where N represents the total number of generated images, where Vobject and Uscene denote the object and scene classifiers, respectively.
Sem-D (Semantic Diversity). To measure semantic diversity, randomly sample P. pairs of generated images for each emotion category and compute the Mean Squared Error (MSE) between their CLIP image embeddings for each pair, averaging the results to obtain the Sem-D score for that emotion. The overall Sem-D metric is then obtained by averaging these scores across all emotion categories, formalized below:
Sem-D CP.
1
ΣΣ MSE (CLIP)(a)), CLIP) =11=1
where C' denotes the total number of emotion categories, P; represents the number of sampled image pairs for the i-th emotion, and CLIP, indicates the CLIP image encoder.
D THE USE OF LLMS
In this work, LLMs were used assist with grammar checking and sentence refinement improve the clarity and overall readability of the manuscript.
15

---

Published as a conference paper at ICLR 2026
"mussmmett
Awe"
Stoble Diffusion
Textual Inversion
DreamBacth
EmoGen
w/o Lara
w/o amotion- specific LoRA
w/ a polority-
shared LORA
CoEnoGen (Ours)
Figure 10: Qualitative comparisons of the proposed CoEmoGen with state-of-the-art generation methods and ablation variants on amusement and awe emotions.
16

---

Published as a conference paper at ICLR 2026
"Contentment"
"Excitement"
Stoble Diffusion
Textual Inversion
DreamBaoth
EmoGen
w/o Caza
WHLRReS HAR
w/o emotion- specific LoRA
w/ o polority-
shared LORÁ
CoEnoGen (Ours)
Figure 11: Qualitative comparisons of the proposed CoEmoGen with state-of-the-art generation methods and ablation variants on contentment and excitement emotions.
17

---

Published as a conference paper at ICLR 2026
"Anger"
"Disgust"
Stoble Diffusion
Textual Inversion
DreamBacth
Emo6en
AVVI AV
AEBEAS
w/o Caza
CM LUTIBO
w/o emotion- specific LoRA
w/ a polority-
shared LORÁ
CoEnoGen (Ours)
Figure 12: Qualitative comparisons of the proposed CoEmoGen with state-of-the-art generation methods and ablation variants on anger and disgust emotions.
18

---

Published as a conference paper at ICLR 2026
"Fear*
"Sadness"
Stoble Diffusion
PNACS BSBAS AUDE
Textual Inversion
RARURT MAL
DreamBooth
EmoGen
w/o Caza
w/o amotion- specific LoRA
w/ o polority-
shared LORA
CoEmoGen (Ours)
Figure 13: Qualitative comparisons of the proposed CoEmoGen with state-of-the-art generation methods and ablation variants on fear and sadness emotions.
19

---

Published as a conference paper at ICLR 2026
"Amusement"
"Awe" "
"Contentment"
"Farr"
"Anger" "
"Sadness"
Figure 14: Sample examples of each emotion in the constructed EmoArt.
20

---

Published as a conference paper at ICLR 2026
"Amusement"
"Fear" background
ye
sign
greene carousal
low
top clua
piik
Sitting
ow
brighlly
laoking
man
wo mar
smill
young girl blue
leping
carnival ride baby gerygggguuu
Ferris
Fa Firt
one
hing
pur ble bac kg roundh
scamea plate young boy
wheel
aas holding
toy
peopln
| loying
's tranding
surr
front
Burrounded
group
large
Totter
light
furfult
colorfu
preat aange
standing
open
llay
Street
black
spider
bloddy
woman
People
statue
creating
white
face
Tear
Si gitting
bright
blood head
lorge,
man
Standing froet
front
wearing
light
Camen
ground
skeleton
staning
ena
face
person
Wall
red
doll
looking
holding
green
hanging
mouth
paillding
mask
FT?
scary facce Teo
photo mnyttfo
person dersonwaamnn wearing
SIT
Christmass t rree
of Priding people litt ee ginlralee foller cotthen blue
wund and Fllwwr wedding
table picture
auoy
col
window
"nu.gruup
dark
sense fear
blue
room
laying
splayod
light
hand
one
knifae
dark
tree
"Awe" "
"Disgust" "
"reetinn
beauh attiggggt beautiful group
setting
serene S
scene
snow
gerbage
food
disgusting
green bunch
ing scene
black
pile
henging
ended
breathtaking Scenee
yellow
free
bright
Ci Gredttn
eredting
bus
background
enttr snowy
creaiing
longe fove ple
trassss
of
red
ane
blue sky
pile trosh
fromt De when bedy
spide
rock
Lfilled
Scnsé
distpnce
duve
cloudy
Sur rundedd
Seautiful uf sunser
sky Foreground Body
front
larbne
sunset
sun
ocean
Top
Welbwiite
trash including
Table cigarette te
sliging Platue
Tag del
butt
snake
alayingg
bog
background
ground
brighty
prentroo
yellow aa
tede conse diegust
white
water looking
sitting
brorch
platte
bottle
eaft
ffield
Tre halding
stardiing
mountain range mountaii
besutifuu serene roan pren
ain
bartle
stringg geage Pigsss pod blug
for near
dirty
enightly coloreds
Browny
Cisgust
"Contentment"
"Anger" "
young boy
aunty day
gedbe gre
imart
love
appean
Wat
smiling
eackes
radatack
blankee
blus
yellow
face
s showing
fosckground
teeth growd,
ooking angcy
"
anger
red auds
-f aopedd
loroad people
opin
do brihiyy sigh
rront
contentment
one!
feeth ghowhhiwng of
teeth
hond
sense
enjoy enjoying
laving
camera
Swette oover
tre eearsin
looking
kground
sitting
intentmenthoe
gross
lighthrr
staard
rock
Towar
sense
content
W
woman
white
table
deasing
field
perple
walking
lowal
looking
sit
flower
green
Shar
foun girl
Bense
evoking
cliiinl für
front
e
cut perple Hommn
say
anght andilig
mouth
lion
blck
people
pyire
srahsaiing
open
fighting
buran
bur
srand
one
ning
feath
lding
showing
youe
Seew
holding
moking
group
sign
dest
yelling
pustisg
&
pronge
tige!
verging
man
oreehingg ng ssans
flome.
l tetco cni
"Excitement" "
"Sadness"
singing
ifront
blue
people
ggroup
picturel
gane
expressi ing
field Bee ocean amss ansmrettee
man
holding
rainbow
one
weaning
th exciteme teeeeeett riding
laughing
Flag
microphone
white
crowd
shirt
face
woter
group
yours boy
exppres
sady
holding
flyinng
creaa
front
ercking spras
race
stat
young
background
Tressdnness
scose
building
sitting
sad
-
head
baby
look
flower
white photo
Bende
spdness
crying
deen
te Boling
gound
Agenng
aumourded
woman
geadig
bligt aher
running wearing het aat
Two
backgrou koroundd
posing young boy
woman woman weiring
women t
randing
wa the
Smiling
celebrating
man
playines
io perteea the
aake
crowd
frret
sitting
raind
people
"Grgal
headstone
white
looking The sad
oreen
window
cemetery
man
bench
Ther
free handd
sit
Ponny, Fie
black
ai
Prosctiiop opas
yellow Mlack white
oppea"
miching bovker
Figure 15: Word clouds generated from captions corresponding each emotion category.
21


Reviews:
[{"review_id": "jwjE2MlP84", "reviewer": "Reviewer_QKK7", "summary": "The work aims to address two core challenges in the field: first, the difficulty general text-to-image models face in accurately capturing and generating abstract emotional concepts like 'awe' or 'contentment'; and second, the limitations of existing EICG-specific model (EmoGen) that rely on word-level attribute labels for guidance. This reliance not only leads to semantically incoherent results (e.g., illogical combinations of elements) and ambiguous emotional expression.\n\nTo overcome these challenges, CoEmoGen proposes a two-pronged solution. First, it leverages Multimodal Large Language Models (MLLMs) to automatically generate context-rich sentence-level captions, replacing the flawed word-level labels to achieve more coherent and rich semantic guidance. Second, inspired by psychological observations, it designs a novel Hierarchical LoRA (HiLoRA) module. This module refines emotion modeling by using \"polarity-shared LoRAs\" (to capture common features of positive/negative emotions) and \"emotion-specific LoRAs\" (to capture the unique semantics of each emotion).\n\nAccording to the experiments, the advantages of this method are the improvements in both semantic coherence (producing more natural and logical images) and emotional faithfulness. Furthermore, its standardized data construction pipeline (using MLLMs) is proven to be highly scalable, allowing it to be effortlessly applied to entirely new domains, such as artistic paintings—a task.", "questions": "**Question 1 (On the Definition and Focus of EICG):**\n\nHow exactly do the authors define Emotional Image Content Generation (EICG)? The examples provided in the paper (e.g., the word clouds figure) suggest that 'emotional images' encompass at least two distinct categories:\n\n1.  Images that **evoke** a specific emotion *in the viewer* (e.g., serene landscapes for 'Awe').\n2.  Images that **depict** a subject *expressing* an emotion (e.g., a person or animal 'showing teeth' for 'Anger').\n\nDoes the CoEmoGen framework demonstrate a particular focus or preference for one of these aspects (evocation or expression), or does it treat both as equally valid means of achieving an \"emotionally faithful\" generation?\n\n**Question 2 (Critique on the Novelty and Scalability of the MLLM Pipeline):**\n\nThe paper presents its MLLM-based pipeline for generating 'sentence-level captions' as a key advantage for scalability, positioning it as a superior alternative to manual 'word-level labels.' However, the MLLM prompt itself still requires a high-level `emotion` label as a prior. This suggests the method is not fully independent of labels.\n\nThis leads to two concerns:\n\n1.  Could this same \"label-to-caption\" generation process also be applied to the EmoGen baseline with minor adaption, like 'label-to-more-labels'? If so, this would imply the novelty lies in the *guidance signal* (captions) rather than the *pipeline's architecture*.\n2.  Furthermore, the pipeline of using a few high-level labels (like an emotion category) to prompt an LLM to generate richer, descriptive captions is an increasingly common practice in the generative field. Does this component of CoEmoGen truly represent a significant and novel advancement in scalability, or is it more of an effective application of an existing paradigm?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "A substantive assessment of the strengths of the paper, touching on each of the following dimensions: originality, quality, clarity, and significance. We encourage reviewers to be broad in their definitions of originality and significance. For example, originality may arise from a new definition or problem formulation, creative combinations of existing ideas, application to a new domain, or removing limitations from prior results.\n\nThe paper begins by defining a clear objective: precisely identifying the core weaknesses of current EICG methods, namely their semantic incoherence and poor scalability.\n\nFinally, the claims are substantiated by comprehensive and well-designed experiments. The authors go beyond standard quantitative and qualitative comparisons by including crucial ablation studies to justify their architectural choices, a well-executed user study to validate the perceptually-driven claim of \"semantic coherence,\" and a practical demonstration (the EmoArt dataset) to prove the tangible benefits of their scalable pipeline.", "weaknesses": "CoEmoGen doesn't completely escape label dependency. It merely replaces 'fine-grained attribute labels' with a 'coarse-grained emotion label'. Therefore, its scalability is relative; it still requires a dataset pre-annotated with emotions as a starting point, rather than being able to learn from completely unsupervised images.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TTc0uJSizd", "reviewer": "Reviewer_xnJi", "summary": "This paper introduces CoEmoGen, a novel pipeline for Emotional Image Content Generation (EICG) that addresses the shortcomings of existing text-to-image and EICG models. The authors identify that prior methods, which rely on word-level attribute labels for guidance, suffer from semantic incoherence, ambiguity, and limited scalability. CoEmoGen tackles this by making two primary contributions: (1) It utilizes Multimodal Large Language Models (MLLMs) to generate context-rich, sentence-level captions that serve as semantically coherent guidance. (2) It proposes a Hierarchical Low-Rank Adaptation (HiLoRA) module, inspired by psychology, which decouples emotion modeling into polarity-shared LoRAs (for common low-level features) and emotion-specific LoRAs (for high-level semantics).", "questions": "The choice of prompt is ablated, but the choice of MLLM is not. Were different MLLMs (e.g., LLaVA, GPT-5) experimented with for caption generation? How sensitive is the model's final performance (e.g., Emo-A or Sem-C) to the quality, verbosity, and style of the captions generated by different MLLMs?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper correctly identifies a critical flaw in prior EICG work: the reliance on word-level attribute labels leads to semantic incoherence (e.g., unnatural \"collage-like\" images). The shift from isolated word-level guidance to sentence-level semantic guidance is a significant and logical paradigm shift that directly addresses this core problem, resulting in more natural and contextually sound images.\n\n2. The design of the HiLoRA module is well-motivated by the psychological observation that emotions of the same polarity (e.g., positive/negative) share low-level visual features (like brightness) while differing in high-level semantics. This hierarchical decoupling is elegant and is shown to be effective via strong ablation studies, where removing either the polarity-shared or emotion-specific LoRAs leads to performance degradation.", "weaknesses": "1. The entire \"coherent semantic acquisition\" pipeline is fundamentally bottlenecked by the quality of the MLLM used for captioning. The authors acknowledge the risk of MLLM hallucinations and use a heuristic CLIP-based filtering method (discarding the bottom 20% ) to mitigate this. However, this filtering may not be robust enough to catch subtle semantic or emotional inaccuracies, and the model's performance is intrinsically tied to the chosen MLLM's capabilities.\n\n2. While the creation of EmoArt is a strength in terms of scalability, its curation methodology appears flawed. The authors use a classifier pre-trained on EmoSet (natural images) to predict emotions for artistic images from WikiArt. This introduces a significant domain gap. A classifier trained on photos is unlikely to accurately capture the emotional expression in abstract, impressionist, or other non-photorealistic art styles, potentially biasing the resulting dataset.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1gC6wfRx5d", "reviewer": "Reviewer_2zyk", "summary": "The paper proposes CoEmoGen, a semantically coherent and scalable framework for emotional image generation. It leverages MLLM-generated sentence-level captions to provide rich emotional supervision and introduces a psychologically inspired HiLoRA architecture that integrates polarity-level and emotion-specific representations. Experiments show consistent improvements across quantitative metrics and human evaluations, confirming enhanced emotional fidelity and semantic coherence.", "questions": "1. How do the authors ensure the semantic validity and emotional reliability of the MLLM-generated captions, given that no human verification or annotation was conducted?\n\n2. Why did the authors choose not to include the CLIP text encoder in training, or alternatively, to constrain the Visual-Perception Encoder’s output to align with the text embedding space?\n\n3. Given that EmoSet’s visual–emotion bias (e.g., “fear = terrifying faces”) and the frequent presence of neutral phrases in captions (e.g., “mouth open”) may reinforce low-level correlations, how might the authors mitigate such biases to encourage abstract and contextually rich emotional understanding?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The introduction of MLLM-generated sentence-level captions offers an effective and scalable framework for automated emotional annotation.\n\n2. The proposed HiLoRA architecture elegantly integrates shared polarity-level and emotion-specific representations to achieve psychologically grounded emotional modeling.\n\n3. By constructing the EmoArt dataset, the work successfully extends emotional image generation beyond photographic realism into artistic and creative domains.", "weaknesses": "1. The MLLM-generated captions may introduce subtle semantic biases or hallucinations, yet there is no human annotation or validation to assess their linguistic accuracy or emotional authenticity.\n\n2. The use of a frozen CLIP text encoder stabilizes training but limits emotional adaptability, as CLIP’s language space is not optimized for affective or psychological semantics.\n\n3. The visual–emotion bias inherent in EmoSet (e.g., fear = terrifying faces) is amplified by multimodal captions and further reinforced through independent emotion-specific LoRA modules. Without textual correction during inference, these biases are directly reflected in the generated results, leading to symbolic and monotonous emotional expressions.\n\n4. Although sentence-level captions from MLLMs aim to enhance semantic coherence, the frequent appearance of neutral phrases (e.g., mouth open) in the word cloud suggests that the model primarily learns low-level visual co-occurrences rather than abstract emotional semantics, which weakens CoEmoGen’s theoretical contribution and generalization ability in genuine affective generation.\n\n5. The figures and tables in the paper use very small fonts, which significantly hinders readability and clarity of presentation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ydlEGaYbKW", "reviewer": "Reviewer_LzoM", "summary": "The paper introduces CoEmoGen, a diffusion-based approach to generate emotion-conditioned images by utilizing hierarchical LORAs.", "questions": "1. How many images were evaluated via the human study?\n2. How is 'targeted emotion transfer' in Fig. 8 achieved?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Novel approach of using hierarchy for emotions by having two levels, positive/negative for level one and Sad/Happy, etc. for level 2.\n2. The results show the superior performance of the proposed approach compared to the baselines.", "weaknesses": "1. The final adapted weight is simply a sum of these LoRAs, which means both adapters are applied in parallel rather than sequentially. This design is more of a multi-branch or compositional LoRA, rather than a truly hierarchical one. A hierarchical LoRA would involve one LoRA’s output feeding into another, or a structured hierarchy where adapters are activated conditionally or at different network depths.\n2. A simple baseline where the emotion label is fed as input to an LLM, which generates a caption for that emotion, and then is fed to a T2I model is missing. I tried this with Flux and the results were reasonable. The results from this experiment would confirm if the problem requires a specific training approach, thereby strengthening the paper's claim. \n3. Line 425: \"we construct the first large-scale emotional art image dataset\". This is incorrect, as [1,2] have done it before, and the data is manually verified. Maybe better to use this dataset.\n\n[1] ArtEmis: Affective Language for Visual Art: Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov, Mohamed Elhoseiny, Leonidas Guibas\n\n[2] It is Okay to Not be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection: Youssef Mohamed, Faizan Farooq Khan, Kilichbek Haydarov, Mohamed Elhoseiny", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)