  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 dynamiclly 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.
