  Synthesizing novel views of dynamic humans from stationary monocular cameras
is a specialized but desirable setup. This is particularly attractive as it
does not require static scenes, controlled environments, or specialized capture
hardware. In contrast to techniques that exploit multi-view observations, the
problem of modeling a dynamic scene from a single view is significantly more
under-constrained and ill-posed. In this paper, we introduce Neural Motion
Consensus Flow (MoCo-Flow), a representation that models dynamic humans in
stationary monocular cameras using a 4D continuous time-variant function. We
learn the proposed representation by optimizing for a dynamic scene that
minimizes the total rendering error, over all the observed images. At the heart
of our work lies a carefully designed optimization scheme, which includes a
dedicated initialization step and is constrained by a motion consensus
regularization on the estimated motion flow. We extensively evaluate MoCo-Flow
on several datasets that contain human motions of varying complexity, and
compare, both qualitatively and quantitatively, to several baselines and
ablated variations of our methods, showing the efficacy and merits of the
proposed approach. Pretrained model, code, and data will be released for
research purposes upon paper acceptance.
