  Recent advances in synthesizing realistic faces have shown that synthetic
training data can replace real data for various face-related computer vision
tasks. A question arises: how important is realism? Is the pursuit of
photorealism excessive? In this work, we show otherwise. We boost the realism
of our synthetic faces by introducing dynamic skin wrinkles in response to
facial expressions and observe significant performance improvements in
downstream computer vision tasks. Previous approaches for producing such
wrinkles either required prohibitive artist effort to scale across identities
and expressions or were not capable of reconstructing high-frequency skin
details with sufficient fidelity. Our key contribution is an approach that
produces realistic wrinkles across a large and diverse population of digital
humans. Concretely, we formalize the concept of mesh-tension and use it to
aggregate possible wrinkles from high-quality expression scans into albedo and
displacement texture maps. At synthesis, we use these maps to produce wrinkles
even for expressions not represented in the source scans. Additionally, to
provide a more nuanced indicator of model performance under deformations
resulting from compressed expressions, we introduce the 300W-winks evaluation
subset and the Pexels dataset of closed eyes and winks.
