GHOST 2.0: generative high-fidelity one shot transfer of heads
Authors: name: Alexander Groshev, name: Anastasiia Iashchenko, name: Pavel Paramonov, name: Denis Dimitrov, name: Andrey Kuznetsov

While the task of face swapping has recently gained attention in the research
community, a related problem of head swapping remains largely unexplored. In
addition to skin color transfer, head swap poses extra challenges, such as the
need to preserve structural information of the whole head during synthesis and
inpaint gaps between swapped head and background. In this paper, we address
these concerns with GHOST 2.0, which consists of two problem-specific modules.
First, we introduce enhanced Aligner model for head reenactment, which
preserves identity information at multiple scales and is robust to extreme pose
variations. Secondly, we use a Blender module that seamlessly integrates the
reenacted head into the target background by transferring skin color and
inpainting mismatched regions. Both modules outperform the baselines on the
corresponding tasks, allowing to achieve state of the art results in head
swapping. We also tackle complex cases, such as large difference in hair styles
of source and target. Code is available at
https://github.com/ai-forever/ghost-2.0