Abstract. 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.
A concurrent work of Drover et al. REF propose to use adversarial framework to impose a prior on the 3D structure, learned solely from 2D projections. Yet they still utilize the groundtruth 3D poses to generate a large number of synthetic 2D poses for training, which augments the original 1.5M 2D poses in Human3.6M by almost 10 times.
Can 3D Pose be Learned from 2D Projections Alone?
ArXiv
ArXiv
Computer Science