Computer Graphics
TU Braunschweig

Perception of Video Manipulation


Recent advances in deep learning-based techniques enable highly realistic facial video manipulations. We investigate the response of human observers’ on these manipulated videos in order to assess the perceived realness of modified faces and their conveyed emotions.

Facial reenactment and face swapping offer great possibilities in creative fields like the post-processing of movie materials. However, they can also easily be abused to create defamatory video content in order to hurt the reputation of the target. As humans are highly specialized in processing and analyzing faces, we aim to investigate perception towards current facial manipulation techniques. Our insights can guide both the creation of virtual actors with a high perceived realness as well as the detection of manipulations based on explicit and implicit feedback of observers.


Colin Groth, Jan-Philipp Tauscher, Susana Castillo, Marcus Magnor:
Altering the Conveyed Facial Emotion Through Automatic Reenactment of Video Portraits
in Proc. International Conference on Computer Animation and Social Agents (CASA), vol. 1300, Springer, Cham, pp. 128-135, November 2020.

Leslie Wöhler, Jann-Ole Henningson, Susana Castillo, Marcus Magnor:
PEFS: A Validated Dataset for Perceptual Experiments on Face Swap Portrait Videos
in Proc. International Conference on Computer Animation and Social Agents (CASA), vol. 1300, Springer, Cham, pp. 120-127, November 2020.

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