Existing neural head avatars methods have achieved significant progress in the image quality and motion range of portrait animation. However, these methods neglect the computational overhead, and to the best of our knowledge, none is designed to run on mobile devices. This paper presents MobilePortrait, a lightweight one-shot neural head avatars method that reduces learning complexity by integrating external knowledge into both the motion modeling and image synthesis, enabling real-time inference on mobile devices. Specifically, we introduce a mixed representation of explicit and implicit keypoints for precise motion modeling and precomputed visual features for enhanced foreground and background synthesis. With these two key designs and using simple U-Nets as backbones, our method achieves state-of-the-art performance with less than one-tenth the computational demand. It has been validated to reach speeds of over 100 FPS on mobile devices and support both video and audio-driven inputs.
View on arXiv@article{jiang2025_2407.05712,
title={ MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devices },
author={ Jianwen Jiang and Gaojie Lin and Zhengkun Rong and Chao Liang and Yongming Zhu and Jiaqi Yang and Tianyun Zhong },
journal={arXiv preprint arXiv:2407.05712},
year={ 2025 }
}