  We tackle human image synthesis, including human motion imitation, appearance
transfer, and novel view synthesis, within a unified framework. It means that
the model, once being trained, can be used to handle all these tasks. The
existing task-specific methods mainly use 2D keypoints to estimate the human
body structure. However, they only express the position information with no
abilities to characterize the personalized shape of the person and model the
limb rotations. In this paper, we propose to use a 3D body mesh recovery module
to disentangle the pose and shape. It can not only model the joint location and
rotation but also characterize the personalized body shape. To preserve the
source information, such as texture, style, color, and face identity, we
propose an Attentional Liquid Warping GAN with Attentional Liquid Warping Block
(AttLWB) that propagates the source information in both image and feature
spaces to the synthesized reference. Specifically, the source features are
extracted by a denoising convolutional auto-encoder for characterizing the
source identity well. Furthermore, our proposed method can support a more
flexible warping from multiple sources. To further improve the generalization
ability of the unseen source images, a one/few-shot adversarial learning is
applied. In detail, it firstly trains a model in an extensive training set.
Then, it finetunes the model by one/few-shot unseen image(s) in a
self-supervised way to generate high-resolution (512 x 512 and 1024 x 1024)
results. Also, we build a new dataset, namely iPER dataset, for the evaluation
of human motion imitation, appearance transfer, and novel view synthesis.
Extensive experiments demonstrate the effectiveness of our methods in terms of
preserving face identity, shape consistency, and clothes details. All codes and
dataset are available on
https://impersonator.org/work/impersonator-plus-plus.html.
