Guided Fine‐Tuning for Large‐Scale Material Transfer

• Published in: Computer graphics forum 2020-07, Vol.39 (4), p.91-105
• Main Authors: Deschaintre, Valentin, Drettakis, George, Bousseau, Adrien
• Format: Article
• Language: English
• Subjects: appearance capture; CCS Concepts; Computer Science; Computing methodologies → Reflectance modeling; deep learning; fine tuning; Image enhancement; Image Processing; material capture; material transfer; Murals; Parameters; Pictures; SVBRDF; Target recognition; Workflow
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.14056

We present a method to transfer the appearance of one or a few exemplar SVBRDFs to a target image representing similar materials. Our solution is extremely simple: we fine‐tune a deep appearance‐capture network on the provided exemplars, such that it learns to extract similar SVBRDF values from the target image. We introduce two novel material capture and design workflows that demonstrate the strength of this simple approach. Our first workflow allows to produce plausible SVBRDFs of large‐scale objects from only a few pictures. Specifically, users only need take a single picture of a large surface and a few close‐up flash pictures of some of its details. We use existing methods to extract SVBRDF parameters from the close‐ups, and our method to transfer these parameters to the entire surface, enabling the lightweight capture of surfaces several meters wide such as murals, floors and furniture. In our second workflow, we provide a powerful way for users to create large SVBRDFs from internet pictures by transferring the appearance of existing, pre‐designed SVBRDFs. By selecting different exemplars, users can control the materials assigned to the target image, greatly enhancing the creative possibilities offered by deep appearance capture.