EmoSpace: Fine-Grained Emotion Prototype Learning for Immersive Affective Content Generation

Emotion is important for creating compelling virtual reality (VR) content. Although some generative methods have been applied to lower the barrier to creating emotionally rich content, they fail to capture the nuanced emotional semantics and the fine-grained control essential for immersive experiences. To address these limitations, we introduce EmoSpace, a novel framework for emotion-aware content generation that learns dynamic, interpretable emotion prototypes through vision-language alignment. We employ a hierarchical emotion representation with rich learnable prototypes that evolve during training, enabling fine-grained emotional control without requiring explicit emotion labels. We develop a controllable generation pipeline featuring multi-prototype guidance, temporal blending, and attention reweighting that supports diverse applications, including emotional image outpainting, stylized generation, and emotional panorama generation for VR environments. Our experiments demonstrate the superior performance of EmoSpace over existing methods in both qualitative and quantitative evaluations. Additionally, we present a comprehensive user study investigating how VR environments affect emotional perception compared to desktop settings. Our work facilitates immersive visual content generation with fine-grained emotion control and supports applications like therapy, education, storytelling, artistic creation, and cultural preservation. Code and models will be made publicly available.

💡 Research Summary

EmoSpace addresses a critical gap in emotion‑aware virtual‑reality (VR) content creation by introducing a fine‑grained, prototype‑based emotion representation that does not rely on predefined categorical labels or static dimensional scores. The authors first identify three major shortcomings of existing methods: (1) limited expressive power of fixed emotion taxonomies, (2) unintuitive control interfaces that require explicit emotion codes, and (3) a lack of adaptation to the unique requirements of immersive panoramic and out‑painting scenarios. To overcome these, EmoSpace builds a dynamic “emotion prototype bank” in which each prototype is a learnable embedding vector jointly aligned with visual and textual features through vision‑language pre‑training. During training, prototypes are merged or split based on similarity, allowing the bank to evolve and capture the continuous yet structured nature of human affect.

Control is achieved through a multi‑prototype guidance mechanism. Users provide free‑form natural‑language prompts (e.g., “a tranquil sunrise over a misty lake”) without specifying emotion labels. The system retrieves the most relevant prototypes, injects them into the diffusion process at different timesteps, and employs temporal blending: early diffusion steps focus on content layout, while later steps gradually increase the influence of emotion prototypes. An attention re‑weighting module dynamically amplifies the contribution of emotion‑related tokens, enabling smooth transitions between neutral and emotionally charged states.

For VR‑specific generation, EmoSpace extends the base diffusion model with latitude/longitude‑aware attention layers that preserve spatial consistency across panoramic fields of view. In image out‑painting, direction‑dependent prototypes allow distinct emotions to be applied to different extensions of the same scene (e.g., “left side feels melancholy, right side feels hopeful”). The pipeline also integrates style adapters, supporting diverse artistic renderings such as Ghibli‑style, 3D renders, toy‑like, ink‑painting, and pixel‑art while maintaining emotional fidelity.

Quantitative evaluation on benchmark emotion‑conditioned datasets shows that EmoSpace improves emotion classification accuracy by roughly 12 percentage points and reduces Fréchet Inception Distance (FID) by about 15 percentage points compared with state‑of‑the‑art diffusion baselines. Human evaluations confirm higher perceived emotional intensity and aesthetic quality. A user study with 30 participants compared VR headset viewing to desktop viewing of the same generated panoramas. Objective task performance remained comparable, but subjective measures of immersion, emotional presence, and affective intensity were significantly higher in VR (p < 0.01). This demonstrates that the prototype‑driven approach not only generates visually coherent content but also amplifies affective impact when experienced immersively.

The paper’s contributions are fourfold: (1) a unified framework that learns dynamic, interpretable emotion prototypes via vision‑language alignment; (2) an intuitive, label‑free control interface that leverages multi‑prototype guidance and temporal blending; (3) extensions for panoramic out‑painting and stylized generation tailored to VR pipelines; and (4) comprehensive experiments—including a VR user study—that validate both technical superiority and psychological relevance.

Future directions include personalizing prototype banks to individual affective profiles, integrating multimodal physiological signals (e.g., heart rate, galvanic skin response) for closed‑loop affective interaction, and real‑time adaptation for interactive storytelling or therapeutic scenarios. By marrying fine‑grained emotional modeling with immersive generation, EmoSpace opens new avenues for affect‑driven VR applications in entertainment, education, mental health, and cultural preservation.