EyeBAG: Accurate Control of Eye Blink and Gaze Based on Data
  Augmentation Leveraging Style Mixing
Bryan S. Kim Jeong Young Jeong Wonjong Ryu
Recent developments in generative models have enabled the generation of
photo-realistic human face images, and downstream tasks utilizing face
generation technology have advanced accordingly. However, models for downstream
tasks are yet substandard at eye control (e.g. eye blink, gaze redirection). To
overcome such eye control problems, we introduce a novel framework consisting
of two distinct modules: a blink control module and a gaze redirection module.
We also propose a novel data augmentation method to train each module,
leveraging style mixing to obtain images with desired features. We show that
our framework produces eye-controlled images of high quality, and demonstrate
how it can be used to improve the performance of downstream tasks.