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Estimating 3D human pose from a single image suffers from severe ambiguitysince multiple 3D joint configurations may have the same 2D projection. Thestate-of-the-art methods often rely on context modeling methods such aspictorial structure model (PSM) or graph neural network (GNN) to reduceambiguity. However, there is no study that rigorously compares them side byside. So we first present a general formula for context modeling in which bothPSM and GNN are its special cases. By comparing the two methods, we found thatthe end-to-end training scheme in GNN and the limb length constraints in PSMare two complementary factors to improve results. To combine their advantages,we propose ContextPose based on attention mechanism that allows enforcing softlimb length constraints in a deep network. The approach effectively reduces thechance of getting absurd 3D pose estimates with incorrect limb lengths andachieves state-of-the-art results on two benchmark datasets. More importantly,the introduction of limb length constraints into deep networks enables theapproach to achieve much better generalization performance.

Context Modeling in 3D Human Pose Estimation: A Unified Perspective