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Unsupervised incremental learning for hand shape and pose estimation

Published: 28 July 2019 Publication History

Abstract

We present an unsupervised incremental learning method for refining hand shape and pose estimation. We propose a refiner network (RefNet) that can augment a state-of-the-art hand tracking system (BaseNet) by refining its estimations on unlabeled data. At each input depth frame, the estimations from the BaseNet are iteratively refined by RefNet using a model-fitting strategy. During this process, the RefNet adapts to the input data characteristics by incremental learning. We show that our method provides more accurate hand shape and pose estimates on both a standard dataset and real data.

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References

[1]
Markus Oberweger, Paul Wohlhart, and Vincent Lepetit. 2015. Training a feedback loop for hand pose estimation. In Proc. IEEE CVPR. 3316--3324.
[2]
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari. 2017. Incremental learning of object detectors without catastrophic forgetting. In Proc. IEEE CVPR. 3400--3409.
[3]
Jonathan Taylor, Lucas Bordeaux, Thomas Cashman, Bob Corish, Cem Keskin, Toby Sharp, Eduardo Soto, David Sweeney, Julien Valentin, Benjamin Luff, et al. 2016. Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences. ACM ToG 35, 4 (2016), 143.
[4]
Anastasia Tkach, Andrea Tagliasacchi, Edoardo Remelli, Mark Pauly, and Andrew Fitzgibbon. 2017. Online generative model personalization for hand tracking. ACM ToG 36, 6 (2017), 243.
[5]
Jonathan Tompson, Murphy Stein, Yann Lecun, and Ken Perlin. 2014. Real-time continuous pose recovery of human hands using convolutional networks. ACM ToG 33 (August 2014), 169:1--169:10.

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Published In

cover image ACM Conferences
SIGGRAPH '19: ACM SIGGRAPH 2019 Posters
July 2019
148 pages
ISBN:9781450363143
DOI:10.1145/3306214
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2019

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Author Tags

  1. hand shape and pose estimation
  2. incremental learning
  3. model fitting

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SIGGRAPH '19
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Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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