Generalized Feedback Loop for Joint Hand-Object Pose Estimation

Markus Oberweger, Paul Wohlhart, Vincent Lepetit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationIEEE transactions on pattern analysis and machine intelligence
Publication statusPublished - 2019

Fingerprint Dive into the research topics of 'Generalized Feedback Loop for Joint Hand-Object Pose Estimation'. Together they form a unique fingerprint.

Cite this