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.

翻译：近期生成模型的发展已能生成照片级真实的人脸图像，利用人脸生成技术的下游任务也随之进步。然而，下游任务模型在眼部控制（如眨眼、视线重定向）方面仍存在不足。为解决此类眼部控制问题，我们提出一个包含两个独立模块的新型框架：眨眼控制模块与视线重定向模块。同时，我们提出一种新颖的数据增强方法，通过利用风格混合技术获取具有期望特征的图像来训练每个模块。实验表明，本框架可生成高质量的眼部控制图像，并展示了其如何用于提升下游任务的性能。