Image-Based Material Editing Using Perceptual Attributes or Ground-Truth Parameters

Abstract

Image-based material editing neural networks have been trained on perceptual attributes because such attributes are human-friendly. But it seems that training such networks on non-perceptual material parameters has been neglected in comparison. It is interesting that collecting perceptual experiment data has been considered an acceptable additional effort until now. It would be much easier to generate a dataset with ground-truth material parameter attributes instead. Ground-truth parameters also avoid the noise that is inherent in perceptual experiment data. We show that existing neural networks can be trained on datasets with ground-truth material parameters and that they generate material edits of similar quality and that stay as close to the valid gamut of the trained material model as neural networks trained on perceptual material attributes. We expect that these results will encourage more study of the qualitative and quantitative differences between image-based material editing networks trained on material parameters and on perceptual attributes.