  Reconstructing 3D hand meshes from monocular RGB images has attracted
increasing amount of attention due to its enormous potential applications in
the field of AR/VR. Most state-of-the-art methods attempt to tackle this task
in an anonymous manner. Specifically, the identity of the subject is ignored
even though it is practically available in real applications where the user is
unchanged in a continuous recording session. In this paper, we propose an
identity-aware hand mesh estimation model, which can incorporate the identity
information represented by the intrinsic shape parameters of the subject. We
demonstrate the importance of the identity information by comparing the
proposed identity-aware model to a baseline which treats subject anonymously.
Furthermore, to handle the use case where the test subject is unseen, we
propose a novel personalization pipeline to calibrate the intrinsic shape
parameters using only a few unlabeled RGB images of the subject. Experiments on
two large scale public datasets validate the state-of-the-art performance of
our proposed method.
