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arXiv · 1903.10883

Generalized Feedback Loop for Joint Hand-Object Pose Estimation

Markus Oberweger·Paul Wohlhart·Vincent Lepetit
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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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Markus Oberweger, Paul Wohlhart, Vincent Lepetit. 2019-03-25. Generalized Feedback Loop for Joint Hand-Object Pose Estimation. https://arxiv.org/abs/1903.10883

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