  Regression-based methods for 3D human pose estimation directly predict the 3D
pose parameters from a 2D image using deep networks. While achieving
state-of-the-art performance on standard benchmarks, their performance degrades
under occlusion. In contrast, optimization-based methods fit a parametric body
model to 2D features in an iterative manner. The localized reconstruction loss
can potentially make them robust to occlusion, but they suffer from the 2D-3D
ambiguity.
  Motivated by the recent success of generative models in rigid object pose
estimation, we propose 3D-aware Neural Body Fitting (3DNBF) - an approximate
analysis-by-synthesis approach to 3D human pose estimation with SOTA
performance and occlusion robustness. In particular, we propose a generative
model of deep features based on a volumetric human representation with Gaussian
ellipsoidal kernels emitting 3D pose-dependent feature vectors. The neural
features are trained with contrastive learning to become 3D-aware and hence to
overcome the 2D-3D ambiguity.
  Experiments show that 3DNBF outperforms other approaches on both occluded and
standard benchmarks. Code is available at https://github.com/edz-o/3DNBF
