Human Pose Estimation by Probabilistic Mixtures of Gaussian and Uniform Distributions
Despite advancements in sub-pixel decoding, modern Human Pose Estimation (HPE) architectures remain constrained by deterministic coordinate extraction from discrete heatmaps, which systematically fails under dense occlusions and Out-of-Distribution (OoD) ambiguities. To address these limitations, we propose a post-hoc continuous probabilistic framework that intercepts raw heatmaps and models them as a Gaussian Mixture Model (GMM) augmented with a spatially agnostic uniform distribution, requiring no architectural modifications or retraining. This continuous representation allows us to comprehensively explore the distinct contributions of density modeling to HPE. We show that the spatial e...
| Authors | Santiago Reina Alguacil, Ezequiel López Rubio |
|---|---|
| Primary category | cs.CV |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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