In this work, we advance the neural head avatar technology to the megapixel
resolution while focusing on the particularly challenging task of cross-driving
synthesis, i.e., when the appearance of the driving image is substantially
different from the animated source image. We propose a set of new neural
architectures and training methods that can leverage both medium-resolution
video data and high-resolution image data to achieve the desired levels of
rendered image quality and generalization to novel views and motion. We
demonstrate that suggested architectures and methods produce convincing
high-resolution neural avatars, outperforming the competitors in the
cross-driving scenario. Lastly, we show how a trained high-resolution neural
avatar model can be distilled into a lightweight student model which runs in
real-time and locks the identities of neural avatars to several dozens of
pre-defined source images. Real-time operation and identity lock are essential
for many practical applications head avatar systems.

本文提出了一种新的神经头像技术，可以以百万像素分辨率完成跨驱动综合任务，其基于新的神经结构和训练方法，可以同时利用中等分辨率视频数据和高分辨率图像数据来生成高质量的头像。通过实验证明，该技术在跨驱动方案中性能更好，并可在实时操作中锁定神经头像的身份，适用于实际应用场景。