Applied Computer Vision
We develop Computer Vision systems that can enhance and support the visual perception and decision making processes of humans. The components we investigate range from low-level image features to high level system components for, e.g., autonomous navigation or artificial scene interpretation.
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Adaptive Human-Machine-Interfaces
We combine Computer Vision, Machine Learning and Computer Graphics with experimental methods of Cognitive Sciences in order to optimize technical interfaces between human and machine perception.
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Automatic Movement Reconstruction Technologies
Rich movement models, for example dynamic morphable face models, have a large impact on the fields of computer vision, behavior monitoring, as well as computer graphics and animation. Attributes such as descriptiveness, semantics, and intuitive control are desirable properties. For robust applications we build models from high-quality and noisy depth data such as that produced by Time-of-Flight (ToF) cameras or devices such as Microsoft Kinect.
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Signal Processing of Social Interactions
We can best understand human behavior in natural interactions. For example, we have developed a real-time facial expression control system. Major technological challenges are in general the reduction of noticeable feedback latencies without loosing accuracy. The inclusion of body motion capture allows to create animatable humans. Learning generative models of interactions is another important goal in order to understand complex behavior. Moreover, we want to understand the visual perception of social crowds, which in return will benefit the development of artificial vision systems in order to make complex predictions.
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