  We propose DeepMultiCap, a novel method for multi-person performance capture
using sparse multi-view cameras. Our method can capture time varying surface
details without the need of using pre-scanned template models. To tackle with
the serious occlusion challenge for close interacting scenes, we combine a
recently proposed pixel-aligned implicit function with parametric model for
robust reconstruction of the invisible surface areas. An effective
attention-aware module is designed to obtain the fine-grained geometry details
from multi-view images, where high-fidelity results can be generated. In
addition to the spatial attention method, for video inputs, we further propose
a novel temporal fusion method to alleviate the noise and temporal
inconsistencies for moving character reconstruction. For quantitative
evaluation, we contribute a high quality multi-person dataset, MultiHuman,
which consists of 150 static scenes with different levels of occlusions and
ground truth 3D human models. Experimental results demonstrate the
state-of-the-art performance of our method and the well generalization to real
multiview video data, which outperforms the prior works by a large margin.
