Google Inc.

Wan-Chun Ma

Paul E Debevec

John Patrick Flynn

Sean Ryan Francesco Fanello

Rohit Kumar Pandey

Graham Fyffe

Konstantine Nicholas John Tsotsos

Chloe LeGendre

Jessica Lynn Busch

Christina Tong

Laurent Charbonnel

Techniques of estimating lighting from portraits includes generating a lighting estimate from a single image of a face based on a machine learning (ML) system using multiple bidirectional reflection distribution functions (BRDFs) as a loss function. In some implementations, the ML system is trained using images of faces formed with HDR illumination computed from LDR imagery. The technical solution includes training a lighting estimation model in a supervised manner using a dataset of portraits and their corresponding ground truth illumination.

Learning illumination from diverse portraits

An example method, apparatus, and computer-readable storage medium are provided to predict high-dynamic range (HDR) lighting from low-dynamic range (LDR) background images. In an example implementation, a method may include receiving low-dynamic range (LDR) background images of scenes, each LDR background image captured with appearance of one or more reference objects with different reflectance properties; and training a lighting estimation model based at least on the received LDR background images to predict high-dynamic range (HDR) lighting based at least on the trained model. In another example implementation, a method may include capturing a low-dynamic range (LDR) background image of a scene from an LDR video captured by a camera of the electronic computing device; predicting high-dynamic range (HDR) lighting for the image, the predicting, using a trained model, based at least on the LDR background image; and rendering a virtual object based at least on the predicted HDR lighting.

Deep light design

Growing community of inventors

Culver City, CA, United States of America

Wan-Chun Ma