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Faithful manipulation of shape, material, and illumination in 2D Internetimages would greatly benefit from a reliable factorization of appearance intomaterial (i.e., diffuse and specular) and illumination (i.e., environmentmaps). On the one hand, current methods that produce very high fidelityresults, typically require controlled settings, expensive devices, orsignificant manual effort. To the other hand, methods that are automatic andwork on 'in the wild' Internet images, often extract only low-frequencylighting or diffuse materials. In this work, we propose to make use of a set ofphotographs in order to jointly estimate the non-diffuse materials and sharplighting in an uncontrolled setting. Our key observation is that seeingmultiple instances of the same material under different illumination (i.e.,environment), and different materials under the same illumination providevaluable constraints that can be exploited to yield a high-quality solution(i.e., specular materials and environment illumination) for all the observedmaterials and environments. Similar constraints also arise when observingmultiple materials in a single environment, or a single material acrossmultiple environments. The core of this approach is an optimization procedurethat uses two neural networks that are trained on synthetic images to predictgood gradients in parametric space given observation of reflected light. Weevaluate our method on a range of synthetic and real examples to generatehigh-quality estimates, qualitatively compare our results againststate-of-the-art alternatives via a user study, and demonstratephoto-consistent image manipulation that is otherwise very challenging toachieve.

Joint Material and Illumination Estimation from Photo Sets in the Wild