  Emerging Metaverse applications demand reliable, accurate, and photorealistic
reproductions of human hands to perform sophisticated operations as if in the
physical world. While real human hand represents one of the most intricate
coordination between bones, muscle, tendon, and skin, state-of-the-art
techniques unanimously focus on modeling only the skeleton of the hand. In this
paper, we present NIMBLE, a novel parametric hand model that includes the
missing key components, bringing 3D hand model to a new level of realism. We
first annotate muscles, bones and skins on the recent Magnetic Resonance
Imaging hand (MRI-Hand) dataset and then register a volumetric template hand
onto individual poses and subjects within the dataset. NIMBLE consists of 20
bones as triangular meshes, 7 muscle groups as tetrahedral meshes, and a skin
mesh. Via iterative shape registration and parameter learning, it further
produces shape blend shapes, pose blend shapes, and a joint regressor. We
demonstrate applying NIMBLE to modeling, rendering, and visual inference tasks.
By enforcing the inner bones and muscles to match anatomic and kinematic rules,
NIMBLE can animate 3D hands to new poses at unprecedented realism. To model the
appearance of skin, we further construct a photometric HandStage to acquire
high-quality textures and normal maps to model wrinkles and palm print.
Finally, NIMBLE also benefits learning-based hand pose and shape estimation by
either synthesizing rich data or acting directly as a differentiable layer in
the inference network.
