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Unsupervised learning of translucent appearances

Translucency is an essential visual phenomenon. The high variability of translucent appearances poses a challenge to understanding the perceptual mechanism. Via unsupervised learning, this work proposed an image-computable framework that discovers a multiscale latent representation, which can disentangle scene attributes related to human translucency perception. Navigating the learned latent space enables coherent editing of the translucent appearances. Liao et al. discovered that translucent impressions emerge in the images' relatively coarse spatial scale features (columns 3 and 4), suggesting that learning the scale-specific statistical structure of natural images may facilitate the representation of material properties across contexts. Liao et al 2023

Image Credit: Chenxi Liao

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