“Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks” by Tompson, Stein, LeCun and Perlin

  • ©Jonathan Tompson, Murphy Stein, Yann LeCun, and Ken Perlin

Conference:


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Title:

    Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks

Session/Category Title: Animating Characters


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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.

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