Nikolina Kubiak
Academic and research departments
Centre for Vision, Speech and Signal Processing (CVSSP), Department of Electrical and Electronic Engineering.About
My research project
Computational lighting in videoThis project explores how advances in generative networks and deep-learning approaches to beautification and stylistic transfer can be combined to artificially recreate the appearance of professional lighting.
Supervisors
This project explores how advances in generative networks and deep-learning approaches to beautification and stylistic transfer can be combined to artificially recreate the appearance of professional lighting.
Publications
We present SILT, a Self-supervised Implicit Lighting Transfer method. Unlike previous research on scene relighting, we do not seek to apply arbitrary new lighting configurations to a given scene. Instead, we wish to transfer the lighting style from a database of other scenes, to provide a uniform lighting style regardless of the input. The solution operates as a two-branch network that first aims to map input images of any arbitrary lighting style to a unified domain, with extra guidance achieved through implicit image decomposition. We then remap this unified input domain using a discriminator that is presented with the generated outputs and the style reference, i.e. images of the desired illumination conditions. Our method is shown to outperform supervised relighting solutions across two different datasets without requiring lighting supervision.