  We target cross-domain face reenactment in this paper, i.e., driving a
cartoon image with the video of a real person and vice versa. Recently, many
works have focused on one-shot talking face generation to drive a portrait with
a real video, i.e., within-domain reenactment. Straightforwardly applying those
methods to cross-domain animation will cause inaccurate expression transfer,
blur effects, and even apparent artifacts due to the domain shift between
cartoon and real faces. Only a few works attempt to settle cross-domain face
reenactment. The most related work AnimeCeleb requires constructing a dataset
with pose vector and cartoon image pairs by animating 3D characters, which
makes it inapplicable anymore if no paired data is available. In this paper, we
propose a novel method for cross-domain reenactment without paired data.
Specifically, we propose a transformer-based framework to align the motions
from different domains into a common latent space where motion transfer is
conducted via latent code addition. Two domain-specific motion encoders and two
learnable motion base memories are used to capture domain properties. A source
query transformer and a driving one are exploited to project domain-specific
motion to the canonical space. The edited motion is projected back to the
domain of the source with a transformer. Moreover, since no paired data is
provided, we propose a novel cross-domain training scheme using data from two
domains with the designed analogy constraint. Besides, we contribute a cartoon
dataset in Disney style. Extensive evaluations demonstrate the superiority of
our method over competing methods.
