  To enable realistic shape (e.g. pose and expression) transfer, existing face
reenactment methods rely on a set of target faces for learning subject-specific
traits. However, in real-world scenario end-users often only have one target
face at hand, rendering existing methods inapplicable. In this work, we bridge
this gap by proposing a novel one-shot face reenactment learning framework. Our
key insight is that the one-shot learner should be able to disentangle and
compose appearance and shape information for effective modeling. Specifically,
the target face appearance and the source face shape are first projected into
latent spaces with their corresponding encoders. Then these two latent spaces
are associated by learning a shared decoder that aggregates multi-level
features to produce the final reenactment results. To further improve the
synthesizing quality on mustache and hair regions, we additionally propose
FusionNet which combines the strengths of our learned decoder and the
traditional warping method. Extensive experiments show that our one-shot face
reenactment system achieves superior transfer fidelity as well as identity
preserving capability than alternatives. More remarkably, our approach trained
with only one target image per subject achieves competitive results to those
using a set of target images, demonstrating the practical merit of this work.
Code, models and an additional set of reenacted faces have been publicly
released at the project page.
