Despite the remarkable advancements in head reenactment, the existing methods
face challenges in cross-domain head reenactment, which aims to transfer human
motions to domains outside the human, including cartoon characters. It is still
difficult to extract motion from out-of-domain images due to the distinct
appearances, such as large eyes. Recently, previous work introduced a
large-scale anime dataset called AnimeCeleb and a cross-domain head reenactment
model, including an optimization-based mapping function to translate the human
domain's expressions to the anime domain. However, we found that the mapping
function, which relies on a subset of expressions, imposes limitations on the
mapping of various expressions. To solve this challenge, we introduce a novel
expression domain translation network that transforms human expressions into
anime expressions. Specifically, to maintain the geometric consistency of
expressions between the input and output of the expression domain translation
network, we employ a 3D geometric-aware loss function that reduces the
distances between the vertices in the 3D mesh of the human and anime. By doing
so, it forces high-fidelity and one-to-one mapping with respect to two
cross-expression domains. Our method outperforms existing methods in both
qualitative and quantitative analysis, marking a significant advancement in the
field of cross-domain head reenactment.

通过引入一种新的表情领域转换网络，该网络将人类表情转化为动漫表情，并使用三维几何感知损失函数来保持输入和输出之间的几何一致性，从而在跨表情领域上实现高保真度和一对一的映射，该方法在跨领域头部再现领域取得了重大进展。