Abstract

Designing effective lighting is an iterative and often time-consuming process. This work contributes to automatic lighting design research by presenting a render-engine agnostic optimization routine: gradient descent on RGB multipliers of one-light-at-a-time (OLAT) basis images. We compare several objective functions to accomplish lighting tasks and show that our method is capable of quickly and effectively exploring different lighting styles using either text prompts or reference images. We also present several datasets specific to lighting tasks and show that fine-tuning on these datasets can improve performance.

Degree

College and Department

Computer Science; Computational, Mathematical, and Physical Sciences

Rights

https://lib.byu.edu/about/copyright/