  Creating plausible virtual actors from images of real actors remains one of
the key challenges in computer vision and computer graphics. Marker-less human
motion estimation and shape modeling from images in the wild bring this
challenge to the fore. Although the recent advances on view synthesis and
image-to-image translation, currently available formulations are limited to
transfer solely style and do not take into account the character's motion and
shape, which are by nature intermingled to produce plausible human forms. In
this paper, we propose a unifying formulation for transferring appearance and
retargeting human motion from monocular videos that regards all these aspects.
Our method synthesizes new videos of people in a different context where they
were initially recorded. Differently from recent appearance transferring
methods, our approach takes into account body shape, appearance, and motion
constraints. The evaluation is performed with several experiments using
publicly available real videos containing hard conditions. Our method is able
to transfer both human motion and appearance outperforming state-of-the-art
methods, while preserving specific features of the motion that must be
maintained (e.g., feet touching the floor, hands touching a particular object)
and holding the best visual quality and appearance metrics such as Structural
Similarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS).
