Human motion capture is defined as the process of recording the movement of a person from sensor measurements. For decades video-based MoCap has been realized by placing optical markers on the human body and capture them with several synchronized cameras. While such systems achieve high accuracies they are impractical to apply on a daily basis, for example to improve athletes’ performances, assist with rehabilitation, or enabling human machine interaction.

We simplify motion capture for everybody, mostly requiring only a single camera, by developing machine learning based techniques to accurately capture 3-dimensional human motion. Since this process strongly depends on high quality training data, and such data is at best sparsely available, we have a special focus on weakly supervised learning techniques. 

The publications below give an overview of our latest advances.