Two papers are accepted at CVsports@CVPR 2025: "Leveraging Anthropometric Measurements to Improve Human Mesh Estimation and Ensure Consistent Body Shapes" by Katja Ludwig, Julian Lorenz, Daniel Kienzle, Tuan Bui & Rainer Lienhart as well as "
Efficient 2D to Full 3D Human Pose Uplifting including Joint Rotations" by Katja Ludwig, Yuliia Oksymets, et al.

The paper "Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer" by Daniel Kienzle, Robin Sch?n, Rainer Lienhart, and Shin’Ichi Satoh has been accepted to the CVSports Workshop, held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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For the paper ?SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts” the authors Robin Sch?n, Julian Lorenz, Daniel Kienzle and Rainer Lienhart received the best paper award at the workshop on Computer Vision for Winter Sports. The workshop took place at the WACV 2025 in Tucson, Arizona.

A paper with the title ?SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts” ?by Robin Sch?n, Julian Lorenz, Daniel Kienzle and Rainer Lienhart has been accepted to the Workshop for “Computer Vision for Winter Sports (CV4WS)”. This workshop will take place in context of the WACV 2025 in Tucson, AZ.

The paper "A Fair Ranking and New Model for Panoptic Scene Graph Generation" by Julian Lorenz, Alexander Pest, Daniel Kienzle, Katja Ludwig, and Rainer Lienhart has been accepted for ECCV 2024 as an Oral Paper.

The authors discuss significant flaws in commonly used evaluation protocols for Panoptic Scene Graph Generation. They present a solution to this problem and evaluate existing publications based on the new findings.
Finally, a new state-of-the-art architecture for Panoptic Scene Graph Generation is presented.

More information can be found here: https://lorjul.github.io/fair-psgg/

A paper with the title “Adapting the Segment Anything Model During Usage in Novel Situations” by Robin Sch?n, Julian Lorenz, Katja Ludwig and Rainer Lienhart has been accepted at the workshop for “Efficient Large Vision Models (eLVM)“. The workshop will be held jointly with the CVPR 2024 in Seattle. The paper presents a method for adapting the Segment Anything Model (SAM) during test time without the aid of additional training data. Instead, the method uses information with is generated during usage in order to generate pseudo labels.

The paper "A Review and Efficient Implementation of Scene Graph Generation Metrics" by Julian Lorenz, Robin Sch?n, Katja Ludwig, and Rainer Lienhart is accepted at the Workshop on Scene Graphs and Graph Representation Learning at CVPR 2024.

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The authors review existing scene graph generation metrics and provide precise definitions that were lacking in this field. Additionally, they introduce an efficient and easy to use python package that implements all discussed metrics. To improve comparability of new scene graph generation methods, the authors provide a benchmarking service that enables an easy evaluation of scene graph generation models.

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More information can be found here: https://lorjul.github.io/sgbench/

We are always looking for excellent researchers who are passionate about research and a PhD. The research focus of our chair is machine learning and perception (vision/audio/other sensor modalities). A current research topic is, for instance, "Lifelong and continuous learning in single and multi-agent systems with sporadic human feedback“. Apply (f/m/d) with CV and previous study grades.

The paper titled "Towards Learning Monocular 3D Object Localization Using the Physical Laws of Motion" by Daniel Kienzle, Julian Lorenz, Katja Ludwig and Rainer Lienhart was accepted to the International Conference on 3D Vision (3DV) 2024. The paper describes a new method for localizing objects in 3D without the need for 3D ground truth. Instead, the method uses knowledge of physical laws to learn the task.

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See https://kiedani.github.io/3DV2024/ for additional information on the paper.

The paper “Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes” by Julian Lorenz, Florian Barthel, Daniel Kienzle, and Rainer Lienhart is accepted at the First ICCV Workshop on Scene Graphs and Graph Representation Learning (SG2RL). The authors present Haystack, a new dataset for scene graph generation that tackles current shortcomings when evaluating with current scene graph datasets. Most notably, Haystack contains rare predicate classes and explicit negative annotations. Only through these properties can rare relationships be reliably evaluated. Based on the design of Haystack, the authors introduce three new scene graph metrics that can be used to gain more detailed insights about the prediction of rare predicate classes.

A paper with the title "Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance" by Robin Sch?n, Katja Ludwig and Rainer Lienhart has been accepted to the? 2nd Workshop on Learning with Limited Labelled Data for Image and Video Understanding at the CVPR 2023. In this paper, the authors examine the effect of pseudo depth maps on the segmentation of object types which have not been present in the training data. The targeted objects are indicated by the means of coordinates on the object surfaces. In order to avoid a dependency ground truth depth maps, the depth maps are predicted by networks.