Gaze redirection methods aim to generate realistic human face images with controllable eye movement. However, recent methods often struggle with 3D consistency, efficiency, or quality, limiting their practical applications. In this work, we propose RTGaze, a real-time and high-quality gaze redirection method. Our approach learns a gaze-controllable facial representation from face images and gaze prompts, then decodes this representation via neural rendering for gaze redirection. Additionally, we distill face geometric priors from a pretrained 3D portrait generator to enhance generation quality. We evaluate RTGaze both qualitatively and quantitatively, demonstrating state-of-the-art performance in efficiency, redirection accuracy, and image quality across multiple datasets. Our system achieves real-time, 3D-aware gaze redirection with a feedforward network (~0.06 sec/image), making it 800x faster than the previous state-of-the-art 3D-aware methods.

翻译：视线重定向方法旨在生成具有可控眼球运动的逼真人脸图像。然而，现有方法常面临三维一致性、效率或生成质量的挑战，限制了其实际应用。本研究提出RTGaze，一种实时高质量的视线重定向方法。该方法从人脸图像和视线提示中学习可视线控制的面部表征，并通过神经渲染解码该表征以实现视线重定向。此外，我们从预训练的三维肖像生成器中提取人脸几何先验知识以提升生成质量。通过定性与定量评估，RTGaze在多个数据集上展现出效率、重定向精度和图像质量的先进性能。该系统以前馈网络（约0.06秒/图像）实现实时三维感知视线重定向，速度较先前最优三维感知方法提升800倍。