Free-view Face Relighting Using a Hybrid Parametric Neural Model on a SMALL-OLAT Dataset

Abstract

The development of neural relighting techniques has by far outpaced the rate of their corresponding training data (e.g., OLAT) generation. For example, high-quality relighting from a single portrait image still requires supervision from comprehensive datasets covering broad diversities in gender, race, complexion, and facial geometry. We present a hybrid parametric neural relighting (PN-Relighting) framework for single portrait relighting, using a much smaller OLAT dataset or SMOLAT. At the core of PN-Relighting, we employ parametric 3D faces coupled with appearance inference and implicit material modelling to enrich SMOLAT for handling in-the-wild images.