  Recent work achieved impressive progress towards joint reconstruction of
hands and manipulated objects from monocular color images. Existing methods
focus on two alternative representations in terms of either parametric meshes
or signed distance fields (SDFs). On one side, parametric models can benefit
from prior knowledge at the cost of limited shape deformations and mesh
resolutions. Mesh models, hence, may fail to precisely reconstruct details such
as contact surfaces of hands and objects. SDF-based methods, on the other side,
can represent arbitrary details but are lacking explicit priors. In this work
we aim to improve SDF models using priors provided by parametric
representations. In particular, we propose a joint learning framework that
disentangles the pose and the shape. We obtain hand and object poses from
parametric models and use them to align SDFs in 3D space. We show that such
aligned SDFs better focus on reconstructing shape details and improve
reconstruction accuracy both for hands and objects. We evaluate our method and
demonstrate significant improvements over the state of the art on the
challenging ObMan and DexYCB benchmarks.
