Unsupervised learning reveals interpretable latent representations for translucency perception.

ABSTRACT: Humans constantly assess the appearance of materials to plan actions, such as stepping on icy roads without slipping. Visual inference of materials is important but challenging because a given material can appear dramatically different in various scenes. This problem especially stands out for translucent materials, whose appearance strongly depends on lighting, geometry, and viewpoint. Despite this, humans can still distinguish between different materials, and it remains unsolved how to systematically discover visual features pertinent to material inference from natural images. Here, we develop an unsupervised style-based image generation model to identify perceptually relevant dimensions for translucent material appearances from photographs. We find our model, with its layer-wise latent r

SUBMITTER: Liao C 

PROVIDER: S-EPMC9942964 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature