  Our goal is to transfer the motion of real people from a source video to a
target video with realistic results. While recent advances significantly
improved image-to-image translations, only few works account for body motions
and temporal consistency. However, those focus only on video re-targeting for a
single actor/ for single actors. In this work, we propose a marker-less
approach for multiple-person video-to-video transfer using pose as an
intermediate representation. Given a source video with multiple persons dancing
or working out, our method transfers the body motion of all actors to a new set
of actors in a different video. Differently from recent "do as I do" methods,
we focus specifically on transferring multiple person at the same time and
tackle the related identity switch problem. Our method is able to convincingly
transfer body motion to the target video, while preserving specific features of
the target video, such as feet touching the floor and relative position of the
actors. The evaluation is performed with visual quality and appearance metrics
using publicly available videos with the permission of their owners.
