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cs.CV2026

TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos

Nima Rahmanian, Daniel Kienzle, Thomas Gossard +3

We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides hours of reconstructed singles and doubles gameplay from monocular broadcast videos, featurin…

cs.CV2026

DSFlash: Comprehensive Panoptic Scene Graph Generation in Realtime

Julian Lorenz, Vladyslav Kovganko, Elias Kohout +3

Scene Graph Generation (SGG) aims to extract a detailed graph structure from an image, a representation that holds significant promise as a robust intermediate step for complex dow…

cs.CV2025

Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation

Daniel Kienzle, Katja Ludwig, Julian Lorenz +2

Obtaining the precise 3D motion of a table tennis ball from standard monocular videos is a challenging problem, as existing methods trained on synthetic data struggle to generalize…

cs.CV2025

SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts

Robin Schön, Julian Lorenz, Daniel Kienzle +1

In this paper, we present a novel architecture for interactive segmentation in winter sports contexts. The field of interactive segmentation deals with the prediction of high-quali…

cs.CV2025

MMMS: Multi-Modal Multi-Surface Interactive Segmentation

Robin Schön, Julian Lorenz, Katja Ludwig +2

In this paper, we present a method to interactively create segmentation masks on the basis of user clicks. We pay particular attention to the segmentation of multiple surfaces that…

cs.CV2025

Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer

Daniel Kienzle, Robin Schön, Rainer Lienhart +1

Analyzing a player's technique in table tennis requires knowledge of the ball's 3D trajectory and spin. While, the spin is not directly observable in standard broadcasting videos,…