  Estimating 3D hand pose from single RGB images is a highly ambiguous problem
that relies on an unbiased training dataset. In this paper, we analyze
cross-dataset generalization when training on existing datasets. We find that
approaches perform well on the datasets they are trained on, but do not
generalize to other datasets or in-the-wild scenarios. As a consequence, we
introduce the first large-scale, multi-view hand dataset that is accompanied by
both 3D hand pose and shape annotations. For annotating this real-world
dataset, we propose an iterative, semi-automated `human-in-the-loop' approach,
which includes hand fitting optimization to infer both the 3D pose and shape
for each sample. We show that methods trained on our dataset consistently
perform well when tested on other datasets. Moreover, the dataset allows us to
train a network that predicts the full articulated hand shape from a single RGB
image. The evaluation set can serve as a benchmark for articulated hand shape
estimation.
