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Domain Adaptive Hand Pose Estimation Based on Self-Looping Adversarial Training Strategy.

Rui Jin1, Jianyu Yang1

  • 1School of Rail Transportation, Soochow University, 8 Jixue Road, Xiangcheng District, Suzhou 215100, China.

Sensors (Basel, Switzerland)
|November 26, 2022
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Summary

This study introduces a novel self-looping adversarial training strategy to bridge the domain gap in hand pose estimation. The method improves synthetic-to-real domain adaptation for more accurate predictions.

Keywords:
adversarial trainingdomain adaptationhand pose estimation

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Deep learning has advanced monocular RGB hand pose estimation.
  • Insufficient labeled data is a key limitation.
  • Synthetic datasets offer abundant annotations but suffer from domain gaps.

Purpose of the Study:

  • To address the domain gap between synthetic and realistic datasets for hand pose estimation.
  • To improve the performance of hand pose estimation models using domain adaptation techniques.

Main Methods:

  • A self-looping adversarial training strategy is proposed to reduce the domain gap.
  • A multi-branch network structure is employed.
  • A novel adversarial training strategy tailored for regression tasks is introduced to minimize the output space.

Main Results:

  • The proposed method effectively reduces the domain gap between synthetic and realistic data.
  • Experiments on H3D and STB datasets demonstrate significant performance improvements.
  • The method outperforms existing state-of-the-art domain adaptive techniques.

Conclusions:

  • The self-looping adversarial training strategy is effective for synthetic-to-real domain adaptation in hand pose estimation.
  • The approach enhances prediction accuracy by mitigating domain discrepancies.
  • This work offers a promising solution for leveraging synthetic data in real-world applications.