Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking

Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we propose a Deep Conditional Variational Autoencoder based model that synthesizes diverse anatomically plausible 3D-pose samples conditioned on the estimated 2D-pose... We show that CVAE-based 3D-pose sample set is consistent with the 2D-pose and helps tackling the inherent ambiguity in 2D-to-3D lifting. We propose two strategies for obtaining the final 3D pose- (a) depth-ordering/ordinal relations to score and weight-average the candidate 3D-poses, referred to as OrdinalScore, and (b) with supervision from an Oracle. We report close to state of-the-art results on two benchmark datasets using OrdinalScore, and state-of-the-art results using the Oracle. We also show that our pipeline yields competitive results without paired image-to-3D annotations. The training and evaluation code is available at read more

PDF Abstract ICCV 2019 PDF ICCV 2019 Abstract


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Monocular 3D Human Pose Estimation Human3.6M MultiPoseNet Average MPJPE (mm) 58.0 # 14
Use Video Sequence No # 1
Frames Needed 1 # 1
Need Ground Truth 2D Pose No # 1
3D Human Pose Estimation Human3.6M MultiPoseNet with Oracle Average MPJPE (mm) 46.8 # 25
Multi-Hypotheses 3D Human Pose Estimation Human3.6M Sharma et al. Average MPJPE (mm) 46.8 # 3
Average PMPJPE (mm) 37.3 # 3