  Hand pose estimation from a monocular RGB image is an important but
challenging task. The main factor affecting its performance is the lack of a
sufficiently large training dataset with accurate hand-keypoint annotations. In
this work, we circumvent this problem by proposing an effective method for
generating realistic hand poses and show that state-of-the-art algorithms for
hand pose estimation can be greatly improved by utilizing the generated hand
poses as training data. Specifically, we first adopt an augmented reality (AR)
simulator to synthesize hand poses with accurate hand-keypoint labels. Although
the synthetic hand poses come with precise joint labels, eliminating the need
of manual annotations, they look unnatural and are not the ideal training data.
To produce more realistic hand poses, we propose to blend a synthetic hand pose
with a real background, such as arms and sleeves. To this end, we develop
tonality-alignment generative adversarial networks (TAGANs), which align the
tonality and color distributions between synthetic hand poses and real
backgrounds, and can generate high quality hand poses. We evaluate TAGAN on
three benchmarks, including the RHP, STB, and CMU-PS hand pose datasets. With
the aid of the synthesized poses, our method performs favorably against the
state-of-the-arts in both 2D and 3D hand pose estimations.
