8 citations · 8 across the 6 of their papers we have counts for
13 papers · 1 filter
Detecting Arbitrary Keypoints on Limbs and Skis with Sparse Partly Correct Segmentation Masks
Katja Ludwig, Daniel Kienzle, Julian Lorenz +1
Analyses based on the body posture are crucial for top-class athletes in many sports disciplines. If at all, coaches label only the most important keypoints, since manual annotatio…
Uplift and Upsample: Efficient 3D Human Pose Estimation with Uplifting Transformers
Moritz Einfalt, Katja Ludwig, Rainer Lienhart
The state-of-the-art for monocular 3D human pose estimation in videos is dominated by the paradigm of 2D-to-3D pose uplifting. While the uplifting methods themselves are rather eff…
Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic Segmentation
Sebastian Scherer, Robin Schön, Rainer Lienhart
Semi-supervised learning (SSL) can reduce the need for large labelled datasets by incorporating unlabelled data into the training. This is particularly interesting for semantic seg…
Recognition of Freely Selected Keypoints on Human Limbs
Katja Ludwig, Daniel Kienzle, Rainer Lienhart
Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of keypoints. Standard HPE models trained on such datasets can only detect these keypoints. If more points ar…
Error Bounds of Projection Models in Weakly Supervised 3D Human Pose Estimation
Nikolas Klug, Moritz Einfalt, Stephan Brehm +1
The current state-of-the-art in monocular 3D human pose estimation is heavily influenced by weakly supervised methods. These allow 2D labels to be used to learn effective 3D human…
Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings
Moritz Einfalt, Rainer Lienhart
In this paper we address the problem of motion event detection in athlete recordings from individual sports. In contrast to recent end-to-end approaches, we propose to use 2D human…