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research-article

Generalized Feedback Loop for Joint Hand-Object Pose Estimation

Published: 01 August 2020 Publication History

Abstract

We propose an approach to estimating the 3D pose of a hand, possibly handling an object, given a depth image. We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop. The components of this feedback loop are also Deep Networks, optimized using training data. This approach can be generalized to a hand interacting with an object. Therefore, we jointly estimate the 3D pose of the hand and the 3D pose of the object. Our approach performs en-par with state-of-the-art methods for 3D hand pose estimation, and outperforms state-of-the-art methods for joint hand-object pose estimation when using depth images only. Also, our approach is efficient as our implementation runs in real-time on a single GPU.

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          cover image IEEE Transactions on Pattern Analysis and Machine Intelligence
          IEEE Transactions on Pattern Analysis and Machine Intelligence  Volume 42, Issue 8
          Aug. 2020
          256 pages

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          Published: 01 August 2020

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