Calian, Dan A.Lalonde, Jean-FrançoisGotardo, PauloSimon, TomasMatthews, IainMitchell, KennyGutierrez, Diego and Sheffer, Alla2018-04-142018-04-1420181467-8659https://doi.org/10.1111/cgf.13341https://diglib.eg.org:443/handle/10.1111/cgf13341Image-based lighting has allowed the creation of photo-realistic computer-generated content. However, it requires the accurate capture of the illumination conditions, a task neither easy nor intuitive, especially to the average digital photography enthusiast. This paper presents an approach to directly estimate an HDR light probe from a single LDR photograph, shot outdoors with a consumer camera, without specialized calibration targets or equipment. Our insight is to use a person's face as an outdoor light probe. To estimate HDR light probes from LDR faces we use an inverse rendering approach which employs data-driven priors to guide the estimation of realistic, HDR lighting. We build compact, realistic representations of outdoor lighting both parametrically and in a data-driven way, by training a deep convolutional autoencoder on a large dataset of HDR sky environment maps. Our approach can recover high-frequency, extremely high dynamic range lighting environments. For quantitative evaluation of lighting estimation accuracy and relighting accuracy, we also contribute a new database of face photographs with corresponding HDR light probes. We show that relighting objects with HDR light probes estimated by our method yields realistic results in a wide variety of settings.I.3.7 [Computer Graphics]Three Dimensional Graphics and RealismColorshadingshadowingand textureI.4.8 [Image Processing and Computer Vision]Scene AnalysisShading10.1111/cgf.1334151-61