  The amount and quality of datasets and tools available in the research field
of hand pose and shape estimation act as evidence to the significant progress
that has been made.However, even the datasets of the highest quality, reported
to date, have shortcomings in annotation. We propose a refinement approach,
based on differentiable ray tracing,and demonstrate how a high-quality publicly
available, multi-camera dataset of hands(InterHand2.6M) can become an even
better dataset, with respect to annotation quality. Differentiable ray tracing
has not been employed so far to relevant problems and is hereby shown to be
superior to the approximative alternatives that have been employed in the past.
To tackle the lack of reliable ground truth, as far as quantitative evaluation
is concerned, we resort to realistic synthetic data, to show that the
improvement we induce is indeed significant. The same becomes evident in real
data through visual evaluation.
