Mackenzie Weygandt Mathis — accepted AI-conference papers

2026

FMPose3D: monocular 3D pose estimation via flow matching

Ti Wang, Xiaohang Yu, Mackenzie Weygandt Mathis

CVPR 2026

Monocular 3D pose estimation is fundamentally ill-posed due to depth ambiguity and occlusions, thereby motivating probabilistic methods that generate multiple plausible 3D pose hypotheses. In particular, diffusion-based models have recently demonstrated strong performance, but their iterative denois

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2023

Rethinking Pose Estimation in Crowds: Overcoming the Detection Information Bottleneck and Ambiguity

Mu Zhou, Lucas Stoffl, Mackenzie Weygandt Mathis, Alexander Mathis

ICCV 2023poster

Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms. Current pipelines either use an object detector together with a pose estimator (top-down approach), or localize all body parts first and then link them to predict the pose of individuals (bottom-up)…

Cited by 28 PDF ↗code ↗Scholar ↗