Research Paper#AI, Music Generation, Image Generation, Emotion Recognition🔬 ResearchAnalyzed: Jan 3, 2026 19:00
Music-to-Image Generation with Semantic and Emotion Alignment
Published:Dec 29, 2025 09:10
•1 min read
•ArXiv
Analysis
This paper addresses the challenging problem of generating images from music, aiming to capture the visual imagery evoked by music. The multi-agent approach, incorporating semantic captions and emotion alignment, is a novel and promising direction. The use of Valence-Arousal (VA) regression and CLIP-based visual VA heads for emotional alignment is a key aspect. The paper's focus on aesthetic quality, semantic consistency, and VA alignment, along with competitive emotion regression performance, suggests a significant contribution to the field.
Key Takeaways
- •Proposes a novel multi-agent framework (MESA MIG) for music-to-image generation.
- •Employs semantic captions and emotion alignment to improve image generation.
- •Utilizes VA regression and CLIP-based visual VA heads for emotional alignment.
- •Demonstrates superior performance compared to baseline methods in several key areas.
Reference
“MESA MIG outperforms caption only and single agent baselines in aesthetic quality, semantic consistency, and VA alignment, and achieves competitive emotion regression performance.”