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

Jonathan Tompson, Murphy Stein, Yann Lecun, Ken Perlin

Research output: Contribution to journalArticlepeer-review


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
Issue number5
StatePublished - Aug 1 2014


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

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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