Article ; Online: Learning Perceptual Aesthetics of 3-D Shapes From Multiple Views.
IEEE computer graphics and applications
2022 Volume 42, Issue 1, Page(s) 20–31
Abstract: The quantification of 3-D shape aesthetics has so far focused on specific shape features and manually defined criteria such as the curvature and the rule of thirds. In this article, we built a model of 3-D shape aesthetics directly from human aesthetics ... ...
Abstract | The quantification of 3-D shape aesthetics has so far focused on specific shape features and manually defined criteria such as the curvature and the rule of thirds. In this article, we built a model of 3-D shape aesthetics directly from human aesthetics preference data and show it to be well aligned with human perception of aesthetics. To build this model, we first crowdsource a large number of human aesthetics preferences by showing shapes in pairs in an online study and then use the same to build a 3-D shape multiview-based deep neural network architecture to allow us to learn a measure of 3-D shape aesthetics. In comparison to previous approaches, we do not use any predefined notions of aesthetics to build our model. Our algorithmically computed measure of shape aesthetics is beneficial to a range of applications in graphics such as search, visualization, and scene composition. |
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MeSH term(s) | Esthetics ; Humans ; Learning |
Language | English |
Publishing date | 2022-01-25 |
Publishing country | United States |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ISSN | 1558-1756 |
ISSN (online) | 1558-1756 |
DOI | 10.1109/MCG.2020.3026137 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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