  We present a new end-to-end learning framework to obtain detailed and
spatially coherent reconstructions of multiple people from a single image.
Existing multi-person methods suffer from two main drawbacks: they are often
model-based and therefore cannot capture accurate 3D models of people with
loose clothing and hair; or they require manual intervention to resolve
occlusions or interactions. Our method addresses both limitations by
introducing the first end-to-end learning approach to perform model-free
implicit reconstruction for realistic 3D capture of multiple clothed people in
arbitrary poses (with occlusions) from a single image. Our network
simultaneously estimates the 3D geometry of each person and their 6DOF spatial
locations, to obtain a coherent multi-human reconstruction. In addition, we
introduce a new synthetic dataset that depicts images with a varying number of
inter-occluded humans and a variety of clothing and hair styles. We demonstrate
robust, high-resolution reconstructions on images of multiple humans with
complex occlusions, loose clothing and a large variety of poses and scenes. Our
quantitative evaluation on both synthetic and real-world datasets demonstrates
state-of-the-art performance with significant improvements in the accuracy and
completeness of the reconstructions over competing approaches.
