Unsupervised learning reveals interpretable latent representations for translucency perception
Fig 3
Experiment 2: Material attribute rating.
(A) The user interface of Experiment 2. (B) The distribution of the mean normalized attribute ratings across observers. For each observer, we normalize their attribute ratings to the range of 0 to 1. The x-axis represents the normalized ratings of an attribute averaged over 20 observers, and the y-axis shows the percentage of images. (C) The scatter plots of ratings between a pair of material attributes, with the Pearson correlations shown at the top. All correlation coefficients are statistically significant at the confidence level of 95% (p < 0.001). In both (B) and (C), gray and green colors represent results for real and generated images, respectively. (D) Examples of real and generated images judged to have different levels of translucency. We grouped the images based on the mean normalized translucency rating: high (0.6 to 1), intermediate (0.2 to 0.6), and low (0 to 0.2).