Probing the link between vision and language in material perception using psychophysics and unsupervised learning
Fig 2
Overview of the synthesis pipeline for morphable material appearances.
(A) Training datasets. (B) Transfer learning pipeline. Upon training, we obtained models to generate images from three material classes. We can generate images of a desired material (e.g., soaps) by injecting the latent codes (e.g., wsoap ∈ Wsoap) into the corresponding material generator (e.g., Gsoap). (C) Illustration of cross-category material morphing. By linearly interpolating between a soap and a rock, we obtain a morphed material, “soap-to-rock,” produced from its latent code wsoap−to−rock and generator Gsoap−to−rock. (D) Illustration of the Space of Morphable Material Appearance. (E) Examples of generated images from the Space of Morphable Material Appearance. These images are a subset of stimuli used in our psychophysical experiments, covering two major lighting conditions (i.e., strong and weak lighting).