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Multi-Level Attention Aggregation for Aesthetic Face Relighting
Face relighting is the challenging task of estimating the illumination cast on portrait images by a light source varying in both position and intensity. As shadows are an important aspect of relighting, many prior works focus on estimating accurate shadows using either a shadow mask or face geometry. While these work well, the rendered images do not look aesthetic/photo-realistic. We propose a novel method that combines the features from attention maps at higher resolutions with the lighting information to estimate aesthetic relit images with accurate shadows. We created a new relighting dataset using a synthetic One-Light-At-a-Time (OLAT) lighting rig in Blender software that captures most of the variations encountered in face relighting. Through extensive experimental validation, we show that the performance of our model is better than the current state-of-art face relighting models despite training on a significantly smaller dataset of only synthetic images. We also demonstrate unsupervised domain adaptation from synthetic to real images. We show that our model is able to adapt very well to significantly different out-of-training light source positions.