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We tackle the human motion imitation, appearance transfer, and novel viewsynthesis within a unified framework, which means that the model once beingtrained can be used to handle all these tasks. The existing task-specificmethods mainly use 2D keypoints (pose) to estimate the human body structure.However, they only expresses the position information with no abilities tocharacterize the personalized shape of the individual person and model thelimbs rotations. In this paper, we propose to use a 3D body mesh recoverymodule to disentangle the pose and shape, which can not only model the jointlocation and rotation but also characterize the personalized body shape. Topreserve the source information, such as texture, style, color, and faceidentity, we propose a Liquid Warping GAN with Liquid Warping Block (LWB) thatpropagates the source information in both image and feature spaces, andsynthesizes an image with respect to the reference. Specifically, the sourcefeatures are extracted by a denoising convolutional auto-encoder forcharacterizing the source identity well. Furthermore, our proposed method isable to support a more flexible warping from multiple sources. In addition, webuild a new dataset, namely Impersonator (iPER) dataset, for the evaluation ofhuman motion imitation, appearance transfer, and novel view synthesis.Extensive experiments demonstrate the effectiveness of our method in severalaspects, such as robustness in occlusion case and preserving face identity,shape consistency and clothes details. All codes and datasets are available onhttps://svip-lab.github.io/project/impersonator.html

Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis