  Hand avatars play a pivotal role in a wide array of digital interfaces,
enhancing user immersion and facilitating natural interaction within virtual
environments. While previous studies have focused on photo-realistic hand
rendering, little attention has been paid to reconstruct the hand geometry with
fine details, which is essential to rendering quality. In the realms of
extended reality and gaming, on-the-fly rendering becomes imperative. To this
end, we introduce an expressive hand avatar, named XHand, that is designed to
comprehensively generate hand shape, appearance, and deformations in real-time.
To obtain fine-grained hand meshes, we make use of three feature embedding
modules to predict hand deformation displacements, albedo, and linear blending
skinning weights, respectively. To achieve photo-realistic hand rendering on
fine-grained meshes, our method employs a mesh-based neural renderer by
leveraging mesh topological consistency and latent codes from embedding
modules. During training, a part-aware Laplace smoothing strategy is proposed
by incorporating the distinct levels of regularization to effectively maintain
the necessary details and eliminate the undesired artifacts. The experimental
evaluations on InterHand2.6M and DeepHandMesh datasets demonstrate the efficacy
of XHand, which is able to recover high-fidelity geometry and texture for hand
animations across diverse poses in real-time. To reproduce our results, we will
make the full implementation publicly available at
https://github.com/agnJason/XHand.
