Prof. Dr. Bernhard Egger

Juniorprofessur for Cognitive Computer Vision

I study how humans and machines can model and perceive 3D shapes and faces.

Research Projects

  • Inverse Rendering, 3D from 2D
  • Statistical Shape Modeling
  • Face Capture
  • Multimodal representation learning
  • Generalization in human and machine perception

Current projects

  • Visualisierung von Bewegungsabläufen basierend auf einem biomechanischen Modell

    (Third Party Funds Group – Sub project)

    Overall project: Empathokinästhetische Sensorik - Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen
    Modellbildung und Zustandsbestimmung (EmpkinS)
    Term: 1. July 2021 - 30. June 2025
    Funding source: DFG / Sonderforschungsbereich (SFB)
    URL: https://www.empkins.de/

    Im Teilprojekt B04 werden innovatiive Verfahren zur Visualisierung von Bewegungsabläufen, die mit verschiedenen empathokinästhetischen Sensoren erfasst und/oder für die eine biomechanische Simulation durchgeführt wurde, erforscht. Der Fokus liegt dabei auf Visualisierungen und der Extraktion von Merkmalen, die bei der medizinischen Interpretation und Diagnose unterstützen.

  • Empathokinästhetische Sensorik für Biofeedback bei depressiven Patienten

    (Third Party Funds Group – Sub project)

    Overall project: Empathokinästhetische Sensorik
    Term: 1. July 2021 - 30. June 2025
    Funding source: DFG / Sonderforschungsbereich (SFB)
    URL: https://www.empkins.de/

    The aim of the D02 project is the establishment of empathokinesthetic sensor technology and methods of machine learning as a means for the automatic detection and modification of depression-associated facial expressions, posture, and movement. The aim is to clarify to what extent, with the help of kinesthetic-related modifications influence depressogenic information processing and/or depressive symptoms. First, we will record facial expressions, body posture, and movement relevant to depression with the help of currently available technologies (e.g., RGB and depth cameras, wired EMG, established emotion recognition software) and use them as input parameters for new machine learning models to automatically detect depression-associated affect expressions. Secondly, a fully automated biofeedback paradigm is to be implemented and validated using the project results available up to that point. More ways of real-time feedback of depression-relevant kinaesthesia are investigated. Thirdly, we will research possibilities of mobile use of the biofeedback approach developed up to then.

Recent publications

2022

2021

2020

Related Research Fields

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