Synthesized Paired Data Boosts Facial Manipulation

A research group from the Moscow Institute of Physics and Technology (MIPT) and Russian Internet giant Yandex have proposed a novel image-to-image translation model that uses synthesized input data to enable a "paired" training approach. The model outperforms existing methods in image manipulation and offers researchers a possible solution to the scarcity of paired datasets. Generative adversarial networks (GAN) are one of the most effective methods for realistic image generation. GANs provide many opportunities for image manipulation and morphing, such as transferring the age or gender of a human face. Network architecture types most commonly used for transforming a human face are trained on either paired images (same subject, different time) or unpaired data.