  The dominant majority of 3D models that appear in gaming, VR/AR, and those we
use to train geometric deep learning algorithms are incomplete, since they are
modeled as surface meshes and missing their interior structures. We present a
learning framework to recover the shape interiors (RoSI) of existing 3D models
with only their exteriors from multi-view and multi-articulation images. Given
a set of RGB images that capture a target 3D object in different articulated
poses, possibly from only few views, our method infers the interior planes that
are observable in the input images. Our neural architecture is trained in a
category-agnostic manner and it consists of a motion-aware multi-view analysis
phase including pose, depth, and motion estimations, followed by interior plane
detection in images and 3D space, and finally multi-view plane fusion. In
addition, our method also predicts part articulations and is able to realize
and even extrapolate the captured motions on the target 3D object. We evaluate
our method by quantitative and qualitative comparisons to baselines and
alternative solutions, as well as testing on untrained object categories and
real image inputs to assess its generalization capabilities.
