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http://dx.doi.org/10.15701/kcgs.2020.26.3.87

Neural Relighting using Specular Highlight Map  

Lee, Yeonkyeong (Visual Computing Lab., Kookmin University)
Go, Hyunsung (Visual Computing Lab., Kookmin University)
Lee, Jinwoo (Visual Computing Lab., Kookmin University)
Kim, Junho (Visual Computing Lab., Kookmin University)
Abstract
In this paper, we propose a novel neural relighting that infers a relighted rendering image based on the user-guided specular highlight map. The proposed network utilizes a pre-trained neural renderer as a backbone network learned from the rendered image of a 3D scene with various lighting conditions. We jointly optimize a 3D light position and its associated relighted image by back-propagation, so that the difference between the base image and the relighted image is similar to the user-guided specular highlight map. The proposed method has the advantage of being able to explicitly infer the 3D lighting position, while providing the artists' preferred 2D screen-space interface. The performance of the proposed network was measured under the conditions that can establish ground truths, and the average error rate of light position estimations is 0.11, with the normalized 3D scene size.
Keywords
Neural Relighting; Neural Renderer; Specular Highlight Map; Light Position Estimation;
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