TapType: Ten-finger text entry on everyday surfaces via Bayesian inference

Despite the advent of touchscreens, typing on physical keyboards remains most efficient for entering text, because users can leverage all fingers across a full-size keyboard for convenient typing. As users increasingly type on the go, text input on mobile and wearable devices has had to compromise on full-size typing. In this paper, we present TapType, a mobile text entry system for full-size typing on passive surfaces--without an actual keyboard. From the inertial sensors inside a band on either wrist, TapType decodes and relates surface taps to a traditional QWERTY keyboard layout. The key novelty of our method is to predict the most likely character sequences by fusing the finger probabilities from our Bayesian neural network classifier with the characters' prior probabilities from an n-gram language model. In our online evaluation, participants on average typed 19 words per minute with a character error rate of 0.6% after 30 minutes of training. Expert typists thereby consistently achieved more than 25 WPM at a similar error rate. We demonstrate applications of TapType in mobile use around smartphones and tablets, as a complement to interaction in situated Mixed Reality outside visual control, and as an eyes-free mobile text input method using an audio feedback-only interface.

EgoPoser: Robust Real-Time Ego-Body Pose Estimation in Large Scenes

Full-body ego-pose estimation from head and hand poses alone has become an active area of research to power articulate avatar representation on headset-based platforms. However, existing methods over-rely on the confines of the motion-capture spaces in which datasets were recorded, while simultaneously assuming continuous capture of joint motions and uniform body dimensions. In this paper, we propose EgoPoser, which overcomes these limitations by 1) rethinking the input representation for headset-based ego-pose estimation and introducing a novel motion decomposition method that predicts full-body pose independent of global positions, 2) robustly modeling body pose from intermittent hand position and orientation tracking only when inside a headset's field of view, and 3) generalizing across various body sizes for different users. Our experiments show that EgoPoser outperforms state-of-the-art methods both qualitatively and quantitatively, while maintaining a high inference speed of over 600 fps. EgoPoser establishes a robust baseline for future work, where full-body pose estimation needs no longer rely on outside-in capture and can scale to large-scene environments.