English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Authoring high-quality digital materials is key to realism in 3D rendering.Previous generative models for materials have been trained exclusively onsynthetic data; such data is limited in availability and has a visual gap toreal materials. We circumvent this limitation by proposing PhotoMat: the firstmaterial generator trained exclusively on real photos of material samplescaptured using a cell phone camera with flash. Supervision on individualmaterial maps is not available in this setting. Instead, we train a generatorfor a neural material representation that is rendered with a learned relightingmodule to create arbitrarily lit RGB images; these are compared against realphotos using a discriminator. We then train a material maps estimator to decodematerial reflectance properties from the neural material representation. Wetrain PhotoMat with a new dataset of 12,000 material photos captured withhandheld phone cameras under flash lighting. We demonstrate that our generatedmaterials have better visual quality than previous material generators trainedon synthetic data. Moreover, we can fit analytical material models to closelymatch these generated neural materials, thus allowing for further editing anduse in 3D rendering.

PhotoMat: A Material Generator Learned from Single Flash Photos