  3D pose estimation from a single image is a challenging task in computer
vision. We present a weakly supervised approach to estimate 3D pose points,
given only 2D pose landmarks. Our method does not require correspondences
between 2D and 3D points to build explicit 3D priors. We utilize an adversarial
framework to impose a prior on the 3D structure, learned solely from their
random 2D projections. Given a set of 2D pose landmarks, the generator network
hypothesizes their depths to obtain a 3D skeleton. We propose a novel Random
Projection layer, which randomly projects the generated 3D skeleton and sends
the resulting 2D pose to the discriminator. The discriminator improves by
discriminating between the generated poses and pose samples from a real
distribution of 2D poses. Training does not require correspondence between the
2D inputs to either the generator or the discriminator. We apply our approach
to the task of 3D human pose estimation. Results on Human3.6M dataset
demonstrates that our approach outperforms many previous supervised and weakly
supervised approaches.
