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
this https URL

我们提出了一种新的 Animation CelebHeads（AnimeCeleb）数据集，利用 3D 动画模型作为可控图像采样器，为头部动画重现提供了大量的头部图像和对应的详细姿势注释，借助半自动流水线构建注释系统，通过 AnimeCeleb 进行训练，我们的跨域头部重新表演模型在推断过程中可以轻松地将用户的动作转移到任意的动画头部，证明 AnimeCeleb 对于训练动画头部重现模型的实用性以及我们的跨域头部重新表演模型相比现有最先进的方法的优越性。