  3D hand pose estimation from images has seen considerable interest from the
literature, with new methods improving overall 3D accuracy. One current
challenge is to address hand-to-hand interaction where self-occlusions and
finger articulation pose a significant problem to estimation. Little work has
applied physical constraints that minimize the hand intersections that occur as
a result of noisy estimation. This work addresses the intersection of hands by
exploiting an occupancy network that represents the hand's volume as a
continuous manifold. This allows us to model the probability distribution of
points being inside a hand. We designed an intersection loss function to
minimize the likelihood of hand-to-point intersections. Moreover, we propose a
new hand mesh parameterization that is superior to the commonly used MANO model
in many respects including lower mesh complexity, underlying 3D skeleton
extraction, watertightness, etc. On the benchmark InterHand2.6M dataset, the
models trained using our intersection loss achieve better results than the
state-of-the-art by significantly decreasing the number of hand intersections
while lowering the mean per-joint positional error. Additionally, we
demonstrate superior performance for 3D hand uplift on Re:InterHand and SMILE
datasets and show reduced hand-to-hand intersections for complex domains such
as sign-language pose estimation.
