Unsupervised learning of translucent material appearance using StyleGAN

Chenxi Liao, Bei Xiao, American University, United States; Masataka Sawayama, Inria, France

Posters 1 Poster

Cognitive science

Pacific Ballroom H-O

Presentation Time:
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -7)

Translucent materials show a wide variety of appearances, arising from the complex interaction among generative physical factors (e.g. scattering, geometry, lighting). As the result, it has been difficult to discover generalizable image cues responsible for human perception of translucency across scenes. To mediate this challenge, we train an unsupervised learning model, StyleGAN2-ADA, on unlabeled photographs to generate perceptually realistic and diverse translucent appearances. By analyzing its layer-wise latent space (W+), we discover that the W+ disentangles the physical factors and humans agree with the emerging semantics at different layers. More importantly, we find its middle layers may encode informative image features humans use to perceive translucency across contexts.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
2022 Conference on Cognitive Computational Neuroscience
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Session P-1
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