1 citations · 1 across the 5 of their papers we have counts for
5 papers
ProPLIKS: Probablistic 3D human body pose estimation
Karthik Shetty, Annette Birkhold, Bernhard Egger +4
We present a novel approach for 3D human pose estimation by employing probabilistic modeling. This approach leverages the advantages of normalizing flows in non-Euclidean geometrie…
Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields
Leonhard Rist, Pluvio Stephan, Noah Maul +6
Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses. Visualizing the morphology and appearance of non-planar sparse anatomical struct…
Revealing an Unattractivity Bias in Mental Reconstruction of Occluded Faces using Generative Image Models
Frederik Riedmann, Bernhard Egger, Tim Rohe
Previous studies have shown that faces are rated as more attractive when they are partially occluded. The cause of this observation remains unclear. One explanation is a mental rec…
exploreCOSMOS: Interactive Exploration of Conditional Statistical Shape Models in the Web-Browser
Maximilian Hahn, Bernhard Egger
Statistical Shape Models of faces and various body parts are heavily used in medical image analysis, computer vision and visualization. Whilst the field is well explored with many…
ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development
Benjamin Cappell, Andreas Stoll, Williams Chukwudi Umah +1
Computational models trained on a large amount of natural images are the state-of-the-art to study human vision - usually adult vision. Computational models of infant vision and it…