  This work proposes a novel learning framework for visual hand dynamics
analysis that takes into account the physiological aspects of hand motion. The
existing models, which are simplified joint-actuated systems, often produce
unnatural motions. To address this, we integrate a musculoskeletal system with
a learnable parametric hand model, MANO, to create a new model, MS-MANO. This
model emulates the dynamics of muscles and tendons to drive the skeletal
system, imposing physiologically realistic constraints on the resulting torque
trajectories. We further propose a simulation-in-the-loop pose refinement
framework, BioPR, that refines the initial estimated pose through a multi-layer
perceptron (MLP) network. Our evaluation of the accuracy of MS-MANO and the
efficacy of the BioPR is conducted in two separate parts. The accuracy of
MS-MANO is compared with MyoSuite, while the efficacy of BioPR is benchmarked
against two large-scale public datasets and two recent state-of-the-art
methods. The results demonstrate that our approach consistently improves the
baseline methods both quantitatively and qualitatively.
