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

被引：468

作者：

Tompson, Jonathan ^{[1
]}

Stein, Murphy ^{[1
]}

Lecun, Yann ^{[1
]}

Perlin, Ken ^{[1
]}

机构：

[1] NYU, New York, NY 10012 USA

来源：

ACM TRANSACTIONS ON GRAPHICS | 2014年 / 33卷 / 05期

关键词：

Hand tracking; neural networks; markerless motion capture; analysis-by-synthesis;

D O I：

10.1145/2629500

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页数：10

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