ResearchResearch paperComputer Vision1 source · Oct 6, 2026

Shape-Bayes: Bayesian Inference of Structured Shapes under Visual Ambiguity

To address this, we introduce Shape-Bayes, a probabilistic framework that couples uncertainty-aware visual perception with Bayesian shape reasoning.

Perceiving structured shapes, such as human faces, from pixels is an inherently ambiguous task in real-world conditions.
Yet, shape inference is largely posed as a deterministic regression task predicting fixed spatial coordinates.
We find that deterministic regression is brittle when visual evidence is ambiguous or incomplete; under severe occlusions deterministic models exhibit structural collapse, predicting incoherent shapes or reverting to generic averages.
Rather than forcing point estimates, Shape-Bayes dynamically weights visual evidence against geometric priors to infer a structurally valid shape posterior.

[1]Shape-Bayes: Bayesian Inference of Structured Shapes under Visual Ambiguity
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 07:31 PM
To address this, we introduce Shape-Bayes, a probabilistic framework that couples uncertainty-aware visual perception with Bayesian shape reasoning.
Perceiving structured shapes, such as human faces, from pixels is an inherently ambiguous task in real-world conditions.

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