MegaPortraits: One-shot Megapixel Neural Head Avatars 


ACM Multimedia Conference




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 show that suggested architectures and methods produce convincing high-resolution neural avatars, outperforming 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. Realtime operation and identity lock are essential for many practical applications of neural head avatars.