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Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map.
However, these methods are not intuitive for user interaction and lack precise lighting control.
We introduce LightPainter, a scribble-based relighting system that allows users to interactively manipulate portrait lighting effect with ease.
This is achieved by two conditional neural networks, a delighting module that recovers geometry and albedo optionally conditioned on skin tone, and a scribble-based module for relighting.
To train the relighting module, we propose a novel scribble simulation procedure to mimic real user scribbles, which allows our pipeline to be trained without any human annotations.
We demonstrate high-quality and flexible portrait lighting editing capability with both quantitative and qualitative experiments.
User study comparisons with commercial lighting editing tools also demonstrate consistent user preference for our method.
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