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Found 4 papers, 3 papers with code
Personalized 3D Human Pose and Shape Refinement
To address this issue, we propose to construct dense correspondences between initial human model estimates and the corresponding images that can be used to refine the initial predictions.
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection
We train a normalizing flow for density estimation as a teacher and a conventional feed-forward network as a student to trigger large distances for anomalies: The bijectivity of the normalizing flow enforces a divergence of teacher outputs for anomalies compared to normal data.
Ranked #1 on
Anomaly Detection
on MVTEC 3D-AD
(Detection AUROC metric, using extra
training data)
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection
In industrial manufacturing processes, errors frequently occur at unpredictable times and in unknown manifestations.
Ranked #1 on
Anomaly Detection
on Surface Defect Saliency of Magnetic Tile
(using extra training data)
Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows
3D human pose estimation from monocular images is a highly ill-posed problem due to depth ambiguities and occlusions.
Ranked #46 on
3D Human Pose Estimation
on MPI-INF-3DHP
(PCK metric)
Monocular 3D Human Pose Estimation
Multi-Hypotheses 3D Human Pose Estimation
