  Our work aims to obtain 3D reconstruction of hands and manipulated objects
from monocular videos. Reconstructing hand-object manipulations holds a great
potential for robotics and learning from human demonstrations. The supervised
learning approach to this problem, however, requires 3D supervision and remains
limited to constrained laboratory settings and simulators for which 3D ground
truth is available. In this paper we first propose a learning-free fitting
approach for hand-object reconstruction which can seamlessly handle two-hand
object interactions. Our method relies on cues obtained with common methods for
object detection, hand pose estimation and instance segmentation. We
quantitatively evaluate our approach and show that it can be applied to
datasets with varying levels of difficulty for which training data is
unavailable.
