This project focuses on using electroencephalography (EEG) to analyze the human visual process. Human visual perception is becoming increasingly important in the analyses of rendering methods, animation results, interface design, and visualization techniques. Our work uses EEG data to provide concrete feedback on the perception of rendered videos and images as opposed to user studies that just capture the user's response. Our results so far are very promising. Not only have we been able to detect a reaction to artifacts in the EEG data, but we have also been able to differentiate between artifacts based on the EEG response.
Example workflow: Experiment, raw data, and data evaluation.
FundingThis work is being funded by the German Science Foundation DFG under the Reinhart Koselleck Project "Immersive Digital Reality". Until 2016 it was funded by the European Research Council ERC under contract No. 256941 'Reality CG'.
EEG-based Analysis of the Impact of Familiarity in the Perception of Deepfake Videos
in Proc. IEEE International Conference on Image Processing (ICIP), IEEE, pp. 160-164, September 2021.
Exploring Neural and Peripheral Physiological Correlates of Simulator Sickness
in Computer Animation and Virtual Worlds, vol. 31, no. 4-5, John Wiley & Sons, Inc., pp. e1953 ff., August 2020.
electronic ISSN: 1546-427X
Immersive EEG: Evaluating Electroencephalography in Virtual Reality
in Proc. IEEE Virtual Reality (VR) Workshop, IEEE, pp. 1794-1800, March 2019.
Analysis of Neural Correlates of Saccadic Eye Movements
in Proc. ACM Symposium on Applied Perception (SAP), no. 17, ACM, pp. 17:1-17:9, August 2018.
Comparative analysis of three different modalities for perception of artifacts in videos
in ACM Transactions on Applied Perception, vol. 14, no. 4, ACM, pp. 1-12, September 2017.
How Human Am I? EEG-based Evaluation of Animated Virtual Characters
in Proc. ACM Human Factors in Computing Systems (CHI), ACM, pp. 5098-5108, May 2017.
EEG Based Analysis of the Perception of Computer-Generated Faces
in Proc. European Conference on Visual Media Production (CVMP), ACM, pp. 4:1-4:10, December 2016.
ElectroEncephaloGraphics: a Novel Modality for Graphics Research
PhD thesis, TU Braunschweig, July 2015.
ElectroEncephaloGraphics: Making Waves in Computer Graphics Research
in IEEE Computer Graphics and Applications, vol. 34, no. 6, pp. 46-56, November 2014.
Single Trial EEG Classification of Artifacts in Videos
in ACM Transactions on Applied Perception, vol. 9, no. 3, pp. 12:1-12:15, July 2012.
EEG Analysis of Implicit Human Visual Perception
in Proc. ACM Human Factors in Computing Systems (CHI), pp. 513-516, May 2012.
Assessing the Quality of Compressed Images Using EEG
in Proc. IEEE International Conference on Image Processing (ICIP), pp. 3170-3173, September 2011.
Evaluation of Video Artifact Perception Using Event-Related Potentials
in Proc. ACM Applied Perception in Computer Graphics and Visualization (APGV), p. 5, August 2011.
Accelerating Photo-realistic RT
The goal of this research project is to develop and evaluate new approaches to accelerate photo-realistic ray tracing. Our focus lies on novel acceleration and denoising strategies for fast and memory-efficient photo-realistic rendering. Our research covers various topics from basic research for fast intersection tests to advanced filtering techniques of Monte Carlo simulation-based rendering.
Featuring more than 10 million pixels at 120 Hertz refresh rate, full-body motion capture, as well as real-time gaze tracking, our 5-meter ICG Dome enables us to research peripheral visual perception, to devise comprehensive foveal-peripheral rendering strategies, and to explore multi-user immersive visualization and interaction.
Scope of "Reality CG" is to pioneer a novel approach to modelling, editing and rendering in computer graphics. Instead of manually creating digital models of virtual worlds, Reality CG will explore new ways to achieve visual realism from the kind of approximate models that can be derived from conventional, real-world imagery as input.
Goal of this project is to assess the quality of rendered videos and especially detect those frames that contain visible artifacts, e.g. ghosting, blurring or popping.