Online Optical Marker-based Hand Tracking with Deep Labels

Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH)


Optical marker-based motion capture is the dominant way for obtaining high-fidelity human body animation for special effects, movies, and video games. However, motion capture has seen limited application to the human hand due to the difficulty of automatically identifying (or labeling) identical markers on self-similar fingers. We propose a technique that frames the labeling problem as a keypoint regression problem conducive to a solution using convolutional neural networks. We demonstrate robustness of our labeling solution to occlusion, ghost markers, hand shape, and even motions involving two hands or handheld objects. Our technique is equally applicable to sparse or dense marker sets and can run in real-time to support interaction prototyping with high-fidelity hand tracking and hand presence in virtual reality.

Related Publications

All Publications

NeurIPS - October 22, 2020

Re-Examining Linear Embeddings for High-dimensional Bayesian Optimization

Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy

Journal of Machine Learning Research (JMLR) - September 30, 2019

Bayesian Optimization for Policy Search via Online-Offline Experimentation

Benjamin Letham, Eytan Bakshy

International Workshop on Mutation Analysis at ICST - May 6, 2021

An Empirical Comparison of Mutant Selection Assessment Metrics

Jie M. Zhang, Lingming Zhang, Dan Hao, Lu Zhang, Mark Harman

AISTATS - April 13, 2021

Aligning Time Series on Incomparable Spaces

Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy