Our paper “On the Importance of Conditioning for Privacy-Preserving Data Augmentation” by Julian Lorenz, Katja Ludwig, Valentin Haug, and Rainer Lienhart has been accepted to the Workshop on Safe and Trustworthy Multimodal AI Systems (SafeMM-AI) at the International Conference on Computer Vision 2025 and our paper has been selected for an oral presentation at the conference by the reviewers.

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Our paper “CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations” by Julian Lorenz, Mrunmai Phatak, Robin Sch?n, Katja Ludwig, Nico H?rmann, Annemarie Friedrich, and Rainer Lienhart has been accepted to the 3rd Workshop on Scene Graphs and Graph Representation Learning (SG2RL) at the International Conference on Computer Vision 2025.

The Paper "HOIverse: A Synthetic Scene Graph Dataset With Human Object Interactions" by Mrunmai Vivek Phatak, Julian Lorenz, Nico H?rmann, J?rg H?hner and Rainer Lienhart has been accepted on IEEE?8^th?International Conference on?Multimedia Information Processing and Retrieval (IEEE?MIPR?2025).

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Our paper "Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer" won the Best Paper Award at the 11th International Workshop on Computer Vision in Sports (CVsports)? This workshop took place as part of this year's CVPR 2025 in Nashville, Tennessee.

Congratulations to Katja Ludwig on the successful defense of her dissertation: Human Pose Estimation in Images and Videos for Sports Analytics: 2D Keypoint and 3D Mesh Estimation for Challenging Scenarios and Extreme Poses.

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).

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.