MPJudge: Towards Perceptual Assessment of Music-Induced Paintings
Authors: name: Shiqi Jiang, name: Tianyi Liang, name: Changbo Wang, name: Chenhui Li
Organization: East China Normal University

Music induced painting is a unique artistic practice, where visual artworks
are created under the influence of music. Evaluating whether a painting
faithfully reflects the music that inspired it poses a challenging perceptual
assessment task. Existing methods primarily rely on emotion recognition models
to assess the similarity between music and painting, but such models introduce
considerable noise and overlook broader perceptual cues beyond emotion. To
address these limitations, we propose a novel framework for music induced
painting assessment that directly models perceptual coherence between music and
visual art. We introduce MPD, the first large scale dataset of music painting
pairs annotated by domain experts based on perceptual coherence. To better
handle ambiguous cases, we further collect pairwise preference annotations.
Building on this dataset, we present MPJudge, a model that integrates music
features into a visual encoder via a modulation based fusion mechanism. To
effectively learn from ambiguous cases, we adopt Direct Preference Optimization
for training. Extensive experiments demonstrate that our method outperforms
existing approaches. Qualitative results further show that our model more
accurately identifies music relevant regions in paintings.