Adversarial learning for viewpoints invariant 3D human pose estimation

摘要

2D pose estimation have achieve remarkable performance with deep convolutional neural networks. However 3D pose estimation is current constrained by the limited datasets of 3D annotations. Meanwhile most annotated images are captured using Motion Capture system in lab or certain studio, which has large variations with large-scale monocular 2D pose datasets. We propose an adversarial learning framework, which can learn invariant human pose latent from 3D annotated datasets to optimize the estimation of monocular images with only 2D annotations. However there is large difference in observation coordinates between 2D datasets and 3D datasets, and this viewpoints issue should be separated from invariant pose latent. We add a viewpoints invariant module to automatically regulate observation viewpoints for generated 3D pose, which transforming the generated pose to more suitable observation in the 3D datasets. Our method achieve competitive results on both 2D and 3D benchmarks. (C) 2018 Published by Elsevier Inc.