  We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an
animation head reenactment. Different from previous animation head datasets, we
utilize 3D animation models as the controllable image samplers, which can
provide a large amount of head images with their corresponding detailed pose
annotations. To facilitate a data creation process, we build a semi-automatic
pipeline leveraging an open 3D computer graphics software with a developed
annotation system. After training with the AnimeCeleb, recent head reenactment
models produce high-quality animation head reenactment results, which are not
achievable with existing datasets. Furthermore, motivated by metaverse
application, we propose a novel pose mapping method and architecture to tackle
a cross-domain head reenactment task. During inference, a user can easily
transfer one's motion to an arbitrary animation head. Experiments demonstrate
the usefulness of the AnimeCeleb to train animation head reenactment models,
and the superiority of our cross-domain head reenactment model compared to
state-of-the-art methods. Our dataset and code are available at
https://github.com/kangyeolk/AnimeCeleb.
