Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …

Development of computer vision algorithms using convolutional neural networks and deep
learning has necessitated ever greater amounts of annotated and labelled data to produce …

Camera calibration is a crucial component in the realm of sports analytics as it serves as the
foundation to extract 3D information out of the broadcast images. Despite the significance of …

Soccer broadcast video understanding has been drawing a lot of attention in recent years
within data scientists and industrial companies. This is mainly due to the lucrative potential …

Markerless motion capture and understanding of professional non-daily human movements
is an important yet unsolved task, which suffers from complex motion patterns and severe …

The increasing number of vision-based tracking systems deployed in production have
necessitated fast, robust camera calibration. In the domain of sport, the majority of current …

Deep learning has the potential to revolutionize sports performance, with applications
ranging from perception and comprehension to decision. This paper presents a …

Positional data in team sports is key in evaluating the players' individual and collective
performances. When the sole source of data is a broadcast-like video of the game, an …

Collecting large datasets for investigations into human locomotion is an expensive and labor-
intensive process. Methods for 3D human pose estimation in the wild are becoming …

Dynamic multi-person mesh recovery has been a hot topic in 3D vision recently. However,
few works focus on the multi-person motion capture from uncalibrated cameras, which …