  Reconstructing two-hand interactions from a single image is a challenging
problem due to ambiguities that stem from projective geometry and heavy
occlusions. Existing methods are designed to estimate only a single pose,
despite the fact that there exist other valid reconstructions that fit the
image evidence equally well. In this paper we propose to address this issue by
explicitly modeling the distribution of plausible reconstructions in a
conditional normalizing flow framework. This allows us to directly supervise
the posterior distribution through a novel determinant magnitude
regularization, which is key to varied 3D hand pose samples that project well
into the input image. We also demonstrate that metrics commonly used to assess
reconstruction quality are insufficient to evaluate pose predictions under such
severe ambiguity. To address this, we release the first dataset with multiple
plausible annotations per image called MultiHands. The additional annotations
enable us to evaluate the estimated distribution using the maximum mean
discrepancy metric. Through this, we demonstrate the quality of our
probabilistic reconstruction and show that explicit ambiguity modeling is
better-suited for this challenging problem.
