AI RESEARCH
CIGPose: Causal Intervention Graph Neural Network for Whole-Body Pose Estimation
arXiv CS.CV
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ArXi:2603.09418v1 Announce Type: new State-of-the-art whole-body pose estimators often lack robustness, producing anatomically implausible predictions in challenging scenes. We posit this failure stems from spurious correlations learned from visual context, a problem we formalize using a Structural Causal Model (SCM). The SCM identifies visual context as a confounder that creates a non-causal backdoor path, corrupting the model's reasoning. We