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Recent developments in generative models have enabled the generation ofphoto-realistic human face images, and downstream tasks utilizing facegeneration technology have advanced accordingly. However, models for downstreamtasks are yet substandard at eye control (e.g. eye blink, gaze redirection). Toovercome such eye control problems, we introduce a novel framework consistingof 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 thatour framework produces eye-controlled images of high quality, and demonstratehow it can be used to improve the performance of downstream tasks.

EyeBAG: Accurate Control of Eye Blink and Gaze Based on Data Augmentation Leveraging Style Mixing