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Adversarial Semantic Data Augmentation for Human Pose Estimation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12364)

Abstract

Human pose estimation is the task of localizing body keypoints from still images. The state-of-the-art methods suffer from insufficient examples of challenging cases such as symmetric appearance, heavy occlusion and nearby person. To enlarge the amounts of challenging cases, previous methods augmented images by cropping and pasting image patches with weak semantics, which leads to unrealistic appearance and limited diversity. We instead propose Semantic Data Augmentation (SDA), a method that augments images by pasting segmented body parts with various semantic granularity. Furthermore, we propose Adversarial Semantic Data Augmentation (ASDA), which exploits a generative network to dynamically predict tailored pasting configuration. Given off-the-shelf pose estimation network as discriminator, the generator seeks the most confusing transformation to increase the loss of the discriminator while the discriminator takes the generated sample as input and learns from it. The whole pipeline is optimized in an adversarial manner. State-of-the-art results are achieved on challenging benchmarks. The code has been publicly available at https://github.com/Binyr/ASDA.

Keywords

Pose estimation Semantic data augmentation 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China under grant 61871435 and the Fundamental Research Funds for the Central Universities no. 2019kfyXKJC024.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina
  2. 2.Tencent Youtu LabShanghaiChina

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