Beauty is in the ELBO of the Beholder: A Variational Account of Processing Fluency in Face Perception

2026-08-25

AI Summary

研究通过训练变分自编码器处理四个面部数据集，并评估其在芝加哥面部数据库上的表现，验证了面孔吸引力与感知流畅性之间的关系。

Abstract

Facial attractiveness has been linked to statistical regularities such as symmetry and averageness, suggesting that beauty may depend on the ease with which a face is perceived. We empirically test this hypothesis by training variational autoencoders on four face datasets without attractiveness supervision and evaluating their representations on the 597 faces from the Chicago Face Database. Across models, human attractiveness ratings closely aligns with the direction defined by the VAE evidence lower bound (ELBO) in rate-distortion space. Independently learned latent spaces contain an attractiveness direction that transfers strongly across random initializations and training data. We also find that attractive faces are more prototypical in both shape and latent space. Our results connect classic accounts of aesthetics with learned generative models and provide empirical support for a variational interpretation of the processing fluency theory of aesthetic pleasure.

Problem

• facial attractiveness
• processing fluency
• variational autoencoders
• perception

Innovation

• variational autoencoders
• ELBO
• processing fluency
• latent space
• attractiveness