Andreas Kölsch

"Monocular Human Pose Estimation In The Wild"

Monocular human pose estimation is a fundamental problem in computer vision with a multitude of application areas, such as autonomous driving, medicine, gaming and more. This makes it an active field of research. While most modern approaches are based on deep-learning models which are trained on large-scale datasets, the specific implementations are very different. This is mostly owed to the many distinct dimensions of the problem which depend on the use case or application area. The approaches can be classified into single-person/multi-person, 2D/3D, skeleton/shape, single image/video, constrained/unconstrained environment and online/offline pose estimation methods. The specific subproblems which are given by combinations of aforementioned dimensions pose unique challenges which require a careful algorithm design to address these challenges. In this talk, I will give an overview of some popular methods which were designed for particular use cases and I will present our own approach for single-person in-the-wild 2D pose estimation which combines the usage of convolutional neural networks and graph networks to exploit the inherent graph structure of the human skeleton. Lastly, I will provide an outlook on planned future works.

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