  Synthetic data generation to improve classification performance (data
augmentation) is a well-studied problem. Recently, generative adversarial
networks (GAN) have shown superior image data augmentation performance, but
their suitability in gesture synthesis has received inadequate attention.
Further, GANs prohibitively require simultaneous generator and discriminator
network training. We tackle both issues in this work. We first discuss a novel,
device-agnostic GAN model for gesture synthesis called DeepGAN. Thereafter, we
formulate DeepNAG by introducing a new differentiable loss function based on
dynamic time warping and the average Hausdorff distance, which allows us to
train DeepGAN's generator without requiring a discriminator. Through
evaluations, we compare the utility of DeepGAN and DeepNAG against two
alternative techniques for training five recognizers using data augmentation
over six datasets. We further investigate the perceived quality of synthesized
samples via an Amazon Mechanical Turk user study based on the HYPE benchmark.
We find that DeepNAG outperforms DeepGAN in accuracy, training time (up to 17x
faster), and realism, thereby opening the door to a new line of research in
generator network design and training for gesture synthesis. Our source code is
available at https://www.deepnag.com.
