I am enrolled in the masters program Neural Information Processing at the university of Tuebingen. Before that I got a B. Sc. in Cognitive Science at the university of Osnabrueck.
I am working on multi-person pose estimation and in particular on synthetic data generation. For synthetic data generation we are using the SMPL body model and motion capture data.
In German Conference on Pattern Recognition (GCPR), September 2019 (inproceedings)
Neural networks need big annotated datasets for training. However, manual annotation can be too expensive or even unfeasible for certain tasks, like multi-person 2D pose estimation with severe occlusions. A remedy for this is synthetic data with perfect ground truth. Here we explore two variations of synthetic data for this challenging problem; a dataset with purely synthetic humans, as well as a real dataset augmented with synthetic humans. We then study which approach better generalizes to real data, as well as the influence of virtual humans in the training loss. We observe that not all synthetic samples are equally informative for training, while the informative samples are different for each training stage. To exploit this observation, we employ an adversarial student-teacher framework; the teacher improves the student by providing the hardest samples for its current state as a challenge. Experiments show that this student-teacher framework outperforms all our baselines.
Unser Ziel ist es, die Prinzipien von Wahrnehmen, Lernen und Handeln in autonomen Systemen zu verstehen, die mit komplexen Umgebungen interagieren. Das Verständnis wollen wir nutzen, um künstliche intelligente Systeme zu entwickeln.