Word-gesture production models that can synthesize word-gestures are critical to the training and evaluation of word-gesture keyboard decoders. We propose WordGesture-GAN, a conditional generative adversarial network that takes arbitrary text as input to generate realistic word-gesture movements in both spatial (i.e., (x, y) coordinates of touch points) and temporal (i.e., timestamps of touch points) dimensions. WordGesture-GAN introduces a Variational Auto-Encoder to extract and embed variations of user-drawn gestures into a Gaussian distribution which can be sampled to control variation in generated gestures. Our experiments on a dataset with 38k gesture samples show that WordGesture-GAN outperforms existing gesture production models including the minimum jerk model [37] and the style-transfer GAN [31, 32] in generating realistic gestures. Overall, our research demonstrates that the proposed GAN structure can learn variations in user-drawn gestures, and the resulting WordGesture-GAN can generate word-gesture movement and predict the distribution of gestures. WordGesture-GAN can serve as a valuable tool for designing and evaluating gestural input systems.

Models that can generate touch typing tasks are important to the development of touch typing keyboards. We propose TouchType-GAN, a Conditional Generative Adversarial Network that can simulate locations and time stamps of touch points in touch typing. TouchType-GAN takes arbitrary text as input to generate realistic touch typing both spatially (i.e., (x, y) coordinates of touch points) and temporally (i.e., timestamps of touch points). TouchType-GAN introduces a variational generator that estimates Gaussian Distributions for every target letter to prevent mode collapse. Our experiments on a dataset with 3k typed sentences show that TouchType-GAN outperforms existing touch typing models, including the Rotational Dual Gaussian model [36] for simulating the distribution of touch points, and the Finger-Fitts Euclidean Model [30] for simulating typing time. Overall, our research demonstrates that the proposed GAN structure can learn the distribution of user typed touch points, and the resulting TouchType-GAN can also estimate typing movements. TouchType-GAN can serve as a valuable tool for designing and evaluating touch typing input systems.