  Recently, data-driven single-view reconstruction methods have shown great
progress in modeling 3D dressed humans. However, such methods suffer heavily
from depth ambiguities and occlusions inherent to single view inputs. In this
paper, we address such issues by lifting the single-view input with additional
views and investigate the best strategy to suitably exploit information from
multiple views. We propose an end-to-end approach that learns an implicit 3D
representation of dressed humans from sparse camera views. Specifically, we
introduce two key components: first an attention-based fusion layer that learns
to aggregate visual information from several viewpoints; second a mechanism
that encodes local 3D patterns under the multi-view context. In the
experiments, we show the proposed approach outperforms the state of the art on
standard data both quantitatively and qualitatively. Additionally, we apply our
method on real data acquired with a multi-camera platform and demonstrate our
approach can obtain results comparable to multi-view stereo with dramatically
less views.
