  When there is a mismatch between the target identity and the driver identity,
face reenactment suffers severe degradation in the quality of the result,
especially in a few-shot setting. The identity preservation problem, where the
model loses the detailed information of the target leading to a defective
output, is the most common failure mode. The problem has several potential
sources such as the identity of the driver leaking due to the identity
mismatch, or dealing with unseen large poses. To overcome such problems, we
introduce components that address the mentioned problem: image attention block,
target feature alignment, and landmark transformer. Through attending and
warping the relevant features, the proposed architecture, called MarioNETte,
produces high-quality reenactments of unseen identities in a few-shot setting.
In addition, the landmark transformer dramatically alleviates the identity
preservation problem by isolating the expression geometry through landmark
disentanglement. Comprehensive experiments are performed to verify that the
proposed framework can generate highly realistic faces, outperforming all other
baselines, even under a significant mismatch of facial characteristics between
the target and the driver.
