  In recent advances of deep generative models, face reenactment -manipulating
and controlling human face, including their head movement-has drawn much
attention for its wide range of applicability. Despite its strong
expressiveness, it is inevitable that the models fail to reconstruct or
accurately generate unseen side of the face of a given single reference image.
Most of existing methods alleviate this problem by learning appearances of
human faces from large amount of data and generate realistic texture at
inference time. Rather than completely relying on what generative models learn,
we show that simple extension by using multiple reference images significantly
improves generation quality. We show this by 1) conducting the reconstruction
task on publicly available dataset, 2) conducting facial motion transfer on our
original dataset which consists of multi-person's head movement video
sequences, and 3) using a newly proposed evaluation metric to validate that our
method achieves better quantitative results.
