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Depth guided any-to-any image relighting aims to generate a relit image fromthe original image and corresponding depth maps to match the illuminationsetting of the given guided image and its depth map. To the best of ourknowledge, this task is a new challenge that has not been addressed in theprevious literature. To address this issue, we propose a deep learning-basedneural Single Stream Structure network called S3Net for depth guided imagerelighting. This network is an encoder-decoder model. We concatenate all imagesand corresponding depth maps as the input and feed them into the model. Thedecoder part contains the attention module and the enhanced module to focus onthe relighting-related regions in the guided images. Experiments performed onchallenging benchmark show that the proposed model achieves the 3 rd highestSSIM in the NTIRE 2021 Depth Guided Any-to-any Relighting Challenge.

S3Net: A Single Stream Structure for Depth Guided Image Relighting