When Visibility Degrades Together: Evaluating Human Pose Estimation across Severity and Compositional Low-Visibility Conditions

Human pose estimation (HPE) rarely operates under a single isolated visibility degradation, yet existing HPE evaluations typically treat each low-visibility condition as an independent fixed-difficulty bucket. They either isolate degradations into separate benchmarks or lack explicit low-visibility annotations, making severity-aware and compositional robustness analysis difficult. We introduce LV-Pose, a diagnostic evaluation framework for analyzing 2D HPE under severity-aware and compositional low-visibility conditions. LV-Pose reveals robustness failures that remain hidden under existing evaluation protocols. Individually mild degradations can compound when they co-occur, while performance within a single condition can deteriorate sharply as visibility severity increases. LV-Pose groups images into three difficulty levels (easy, medium, hard). Keypoint and severity annotations are provided for three primary conditions, haze/fog, low-light/nighttime, and precipitation, together with image-level tags for additional co-occurring visibility conditions. LV-Pose therefore supports severity-conditioned and composition-conditioned analysis of HPE robustness, instead of a single aggregate robustness claim under low visibility. Across representative HPE estimators and foundation models such as vision foundation models and vision-language models, LV-Pose reveals strong severity-dependent robustness degradation and compounding failures where co-occurring degradations reduce accuracy beyond their isolated effects. Restoration preprocessing also fails to consistently improve downstream pose robustness across severity levels, and can even degrade strong HPE systems despite visually enhanced inputs.