DATA-BASED AESTHETICS: QUANTIFYING BEAUTY IN SCULPTURAL FORMS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7102Keywords:
Computational Aesthetics, Sculptural Beauty Quantification, 3D Shape Analysis, Machine Learning in Art, Digital Cultural HeritageAbstract [English]
Data-based aesthetics is a new interdisciplinary paradigm, which aims to measure the beauty of art by providing such computational descriptors that can be quantified. Aesthetic judgment, in terms of sculptural forms, has always been subjective, based on cultural, historical, and perceptual influences, as well. The current paper suggests a systematic approach to measuring beauty in the art of sculpture, a combination of geometric analysis of beauty, mathematical theory of proportion, and aesthetic prediction with the use of machine learning. The study proposes a systematic process of processing sculptural objects which are represented by museum collections, digital archives, high-resolution 3D scans to analyzable data formats. Major features of sculpture are broken down into geometric, structural, and surface features such as continuity of curvature, indices of symmetry, balance of masses, alignment of center-of-gravity, and roughness of texture. A mathematical concept of aesthetic beauty is created in order to combine the rules based on proportions, like the golden ratio compliance, with statistical and learned weights of features. Classical regression models, as well as deep learning architectures (3D convolutional neural network and point-based neural model), are used to predict sculptural features on continuous aesthetic scores. This mixed method allows relating the aesthetic principles of handcrafting with the learning of features based on data. It has been shown experimentally that multi-level features representations are much stronger predictors than single domain descriptors.
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Copyright (c) 2026 Sneha Mhatre, Sunita Naik, Sanket Patil, Ashwini Save, Tatwadarshi P. Nagarhalli
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