Publication

Online Optical Marker-based Hand Tracking with Deep Labels

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


Abstract

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

Uncertainty and Robustness in Deep Learning Workshop at ICML - August 1, 2020

Tilted Empirical Risk Minimization

Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

arxiv - November 1, 2020

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes

Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, Davide Testuggine

ICML - July 24, 2021

Using Bifurcations for Diversity in Differentiable Games

Jonathan Lorraine, Jack Parker-Holder, Paul Vicol, Aldo Pacchiano, Luke Metz, Tal Kachman, Jakob Foerster

IEEE WHC - July 6, 2021

Hasti: Haptic and Audio Synthesis for Texture Interactions

Sonny Chan, Chase Tymms, Nicholas Colonnese

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