  Estimating the 3D hand pose from a monocular RGB image is important but
challenging. A solution is training on large-scale RGB hand images with
accurate 3D hand keypoint annotations. However, it is too expensive in
practice. Instead, we have developed a learning-based approach to synthesize
realistic, diverse, and 3D pose-preserving hand images under the guidance of 3D
pose information. We propose a 3D-aware multi-modal guided hand generative
network (MM-Hand), together with a novel geometry-based curriculum learning
strategy. Our extensive experimental results demonstrate that the 3D-annotated
images generated by MM-Hand qualitatively and quantitatively outperform
existing options. Moreover, the augmented data can consistently improve the
quantitative performance of the state-of-the-art 3D hand pose estimators on two
benchmark datasets. The code will be available at
https://github.com/ScottHoang/mm-hand.
