Deep learning systems, devices, and methods for predicting high-dynamic range environment panoramas

Abstract

Aspects of lighting estimation, and models therefor are provided including aspects to train such models. There is provided a lighting estimation model pre-trained using synthetic data to alleviate the costs and difficulty in obtaining real portrait image and HDR environment map paired datasets. To improve model performance, the model is training utilizing a discriminator configured to predict one or more average color values of a defined percentage of highest intensity pixels of a predicted environment map and to determine a color loss associated with the predicted environment map and the one or more average color values. The trained model can be used for a wide range of downstream tasks, including being used to generate hair renderings with realistic lighting effects for virtual try on experiences.