Music2Palette: Emotion-aligned Color Palette Generation via Cross-Modal Representation Learning

TL;DR

Music2Palette introduces a cross-modal learning approach to generate emotion-aligned color palettes from music, addressing limitations of existing methods by capturing emotion variation and ensuring diversity and coherence.

Contribution

The paper presents a novel dataset MuCED and a cross-modal framework for direct music-to-palette translation with multi-objective optimization for improved emotion alignment and diversity.

Findings

Outperforms existing methods in emotion interpretation and palette quality

Enables applications like music-driven recoloring and visualization

Demonstrates effectiveness through extensive experiments

Abstract

Emotion alignment between music and palettes is crucial for effective multimedia content, yet misalignment creates confusion that weakens the intended message. However, existing methods often generate only a single dominant color, missing emotion variation. Others rely on indirect mappings through text or images, resulting in the loss of crucial emotion details. To address these challenges, we present Music2Palette, a novel method for emotion-aligned color palette generation via cross-modal representation learning. We first construct MuCED, a dataset of 2,634 expert-validated music-palette pairs aligned through Russell-based emotion vectors. To directly translate music into palettes, we propose a cross-modal representation learning framework with a music encoder and color decoder. We further propose a multi-objective optimization approach that jointly enhances emotion alignment, color diversity, and palette coherence. Extensive experiments demonstrate that our method outperforms current methods in interpreting music emotion and generating attractive and diverse color palettes. Our approach enables applications like music-driven image recoloring, video generating, and data visualization, bridging the gap between auditory and visual emotion experiences.