  Transferring human motion and appearance between videos of human actors
remains one of the key challenges in Computer Vision. Despite the advances from
recent image-to-image translation approaches, there are several transferring
contexts where most end-to-end learning-based retargeting methods still perform
poorly. Transferring human appearance from one actor to another is only ensured
when a strict setup has been complied, which is generally built considering
their training regime's specificities. In this work, we propose a shape-aware
approach based on a hybrid image-based rendering technique that exhibits
competitive visual retargeting quality compared to state-of-the-art neural
rendering approaches. The formulation leverages the user body shape into the
retargeting while considering physical constraints of the motion in 3D and the
2D image domain. We also present a new video retargeting benchmark dataset
composed of different videos with annotated human motions to evaluate the task
of synthesizing people's videos, which can be used as a common base to improve
tracking the progress in the field. The dataset and its evaluation protocols
are designed to evaluate retargeting methods in more general and challenging
conditions. Our method is validated in several experiments, comprising publicly
available videos of actors with different shapes, motion types, and camera
setups. The dataset and retargeting code are publicly available to the
community at: https://www.verlab.dcc.ufmg.br/retargeting-motion.
