Liquid Warping GAN With Attention: A Unified Framework for Human Image Synthesis

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 (pose) to estimate the human body structure. However, they only express the position information with no ability 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 first 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 (<inline-formula><tex-math notation="LaTeX">$512 \times 512$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>512</mml:mn><mml:mo>×</mml:mo><mml:mn>512</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-3078270.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$1024 \times 1024$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1024</mml:mn><mml:mo>×</mml:mo><mml:mn>1024</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq2-3078270.gif"/></alternatives></inline-formula>) results. Also, we build a new dataset, namely Impersonator (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 <uri>https://impersonator.org/work/impersonator-plus-plus.html</uri>.