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.

DeepMultiCap 是一种新颖的方法，用于使用稀疏多视角相机进行多人表演捕捉，其中结合了像素对齐的隐式函数和参数模型以实现对无形表面区域的鲁棒重建，以及有效的注意力感知模块以获取多视角图像中的细节。