Real-time continuous pose recovery of human hands using convolutional networks

Jonathan Tompson, Murphy Stein, Yann Lecun, Ken Perlin

Research output: Contribution to journalArticle

Abstract

We present a novel method for real-time continuous pose recovery of markerless complex articulable objects from a single depth image. Our method consists of the following stages: a randomized decision forest classifier for image segmentation, a robust method for labeled dataset generation, a convolutional network for dense feature extraction, and finally an inverse kinematics stage for stable real-time pose recovery. As one possible application of this pipeline, we show state-of-the-art results for real-time puppeteering of a skinned hand-model.

Original languageEnglish (US)
Article number169
JournalACM Transactions on Graphics
Volume33
Issue number5
DOIs
StatePublished - Aug 1 2014

Keywords

  • Analysis-by-synthesis
  • Hand tracking
  • Markerless motion capture
  • Neural networks

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Real-time continuous pose recovery of human hands using convolutional networks'. Together they form a unique fingerprint.

  • Cite this