SelfSwapper: Self-Supervised Face Swapping via Shape Agnostic Masked
  AutoEncoder
Jaeseong Lee Junha Hyung Sohyun Jeong Jaegul Choo
Face swapping has gained significant attention for its varied applications.
The majority of previous face swapping approaches have relied on the seesaw
game training scheme, which often leads to the instability of the model
training and results in undesired samples with blended identities due to the
target identity leakage problem. This paper introduces the Shape Agnostic
Masked AutoEncoder (SAMAE) training scheme, a novel self-supervised approach
designed to enhance face swapping model training. Our training scheme addresses
the limitations of traditional training methods by circumventing the
conventional seesaw game and introducing clear ground truth through its
self-reconstruction training regime. It effectively mitigates identity leakage
by masking facial regions of the input images and utilizing learned
disentangled identity and non-identity features. Additionally, we tackle the
shape misalignment problem with new techniques including perforation confusion
and random mesh scaling, and establishes a new state-of-the-art, surpassing
other baseline methods, preserving both identity and non-identity attributes,
without sacrificing on either aspect.