  Despite recent achievements, existing 3D interacting hands recovery methods
have shown results mainly on motion capture (MoCap) environments, not on
in-the-wild (ITW) ones. This is because collecting 3D interacting hands data in
the wild is extremely challenging, even for the 2D data. We present InterWild,
which brings MoCap and ITW samples to shared domains for robust 3D interacting
hands recovery in the wild with a limited amount of ITW 2D/3D interacting hands
data. 3D interacting hands recovery consists of two sub-problems: 1) 3D
recovery of each hand and 2) 3D relative translation recovery between two
hands. For the first sub-problem, we bring MoCap and ITW samples to a shared 2D
scale space. Although ITW datasets provide a limited amount of 2D/3D
interacting hands, they contain large-scale 2D single hand data. Motivated by
this, we use a single hand image as an input for the first sub-problem
regardless of whether two hands are interacting. Hence, interacting hands of
MoCap datasets are brought to the 2D scale space of single hands of ITW
datasets. For the second sub-problem, we bring MoCap and ITW samples to a
shared appearance-invariant space. Unlike the first sub-problem, 2D labels of
ITW datasets are not helpful for the second sub-problem due to the 3D
translation's ambiguity. Hence, instead of relying on ITW samples, we amplify
the generalizability of MoCap samples by taking only a geometric feature
without an image as an input for the second sub-problem. As the geometric
feature is invariant to appearances, MoCap and ITW samples do not suffer from a
huge appearance gap between the two datasets. The code is publicly available at
https://github.com/facebookresearch/InterWild.
