Modeling Aesthetic Preferences in 3D Shapes: A Large-Scale Paired Comparison Study Across Object Categories

TL;DR

This study empirically investigates human aesthetic preferences for 3D shapes across multiple object categories using large-scale pairwise comparisons, revealing geometric features influencing aesthetic judgments and providing insights for design.

Contribution

It introduces a large-scale human judgment dataset, applies novel non-linear modeling and cross-category analysis, and emphasizes interpretable geometric features for understanding 3D shape aesthetics.

Findings

Symmetry and curvature are key aesthetic drivers.

Universal and domain-specific aesthetic principles are identified.

The dataset and models enable practical design insights.

Abstract

Human aesthetic preferences for 3D shapes are central to industrial design, virtual reality, and consumer product development. However, most computational models of 3D aesthetics lack empirical grounding in large-scale human judgments, limiting their practical relevance. We present a large-scale study of human preferences. We collected 22,301 pairwise comparisons across five object categories (chairs, tables, mugs, lamps, and dining chairs) via Amazon Mechanical Turk. Building on a previously published dataset~\cite{dev2020learning}, we introduce new non-linear modeling and cross-category analysis to uncover the geometric drivers of aesthetic preference. We apply the Bradley-Terry model to infer latent aesthetic scores and use Random Forests with SHAP analysis to identify and interpret the most influential geometric features (e.g., symmetry, curvature, compactness). Our cross-category analysis reveals both universal principles and domain-specific trends in aesthetic preferences. We focus on human interpretable geometric features to ensure model transparency and actionable design insights, rather than relying on black-box deep learning approaches. Our findings bridge computational aesthetics and cognitive science, providing practical guidance for designers and a publicly available dataset to support reproducibility. This work advances the understanding of 3D shape aesthetics through a human-centric, data-driven framework.