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

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

Tompson, Jonathan; Stein, Murphy; Lecun, Yann; Perlin, Ken

ACM transactions on graphics, 08/2014, Volume: 33, Issue: 5

Journal Article

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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Tompson, Jonathan | Stein, Murphy | Lecun, Yann | Perlin, Ken

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