  In this paper we introduce a large-scale hand pose dataset, collected using a
novel capture method. Existing datasets are either generated synthetically or
captured using depth sensors: synthetic datasets exhibit a certain level of
appearance difference from real depth images, and real datasets are limited in
quantity and coverage, mainly due to the difficulty to annotate them. We
propose a tracking system with six 6D magnetic sensors and inverse kinematics
to automatically obtain 21-joints hand pose annotations of depth maps captured
with minimal restriction on the range of motion. The capture protocol aims to
fully cover the natural hand pose space. As shown in embedding plots, the new
dataset exhibits a significantly wider and denser range of hand poses compared
to existing benchmarks. Current state-of-the-art methods are evaluated on the
dataset, and we demonstrate significant improvements in cross-benchmark
performance. We also show significant improvements in egocentric hand pose
estimation with a CNN trained on the new dataset.
