In recent years, we have witnessed rising interest across consumers and researchers in integrated augmented reality (AR) experiences using real-time face feature generation and editing functions in mobile applications, including short videos, virtual reality, and gaming. As a result, there is a growing demand for lightweight, yet high-quality face generation and editing models, which are often based on generative adversarial network (GAN) techniques. However, the majority of GAN models suffer from high computational complexity and the need for a large training dataset. In addition, it is also important to employ GAN models responsibly.
On-device content distillation with graph neural networks
