  Video-based human motion transfer creates video animations of humans
following a source motion. Current methods show remarkable results for
tightly-clad subjects. However, the lack of temporally consistent handling of
plausible clothing dynamics, including fine and high-frequency details,
significantly limits the attainable visual quality. We address these
limitations for the first time in the literature and present a new framework
which performs high-fidelity and temporally-consistent human motion transfer
with natural pose-dependent non-rigid deformations, for several types of loose
garments. In contrast to the previous techniques, we perform image generation
in three subsequent stages, synthesizing human shape, structure, and
appearance. Given a monocular RGB video of an actor, we train a stack of
recurrent deep neural networks that generate these intermediate representations
from 2D poses and their temporal derivatives. Splitting the difficult motion
transfer problem into subtasks that are aware of the temporal motion context
helps us to synthesize results with plausible dynamics and pose-dependent
detail. It also allows artistic control of results by manipulation of
individual framework stages. In the experimental results, we significantly
outperform the state-of-the-art in terms of video realism. Our code and data
will be made publicly available.
