Geo-metric: A Perceptual Dataset of Distortions on Faces
DescriptionIn this work we take a novel perception-centered approach to quantify distortions on 3D geometry of faces, to which humans are particularly sensitive. We generated a dataset, composed of 100 high-quality and demographically-balanced face scans. We then subjected these meshes to distortions that cover relevant use cases in computer graphics, and conducted a large-scale perceptual study to subjectively evaluate them. Our dataset consists of over 84,000 quality comparisons, making it the largest ever psychophysical dataset for geometric distortions. Finally, we demonstrate how our data can be used for applications like metrics, compression, and level-of-detail rendering.
Event Type
Technical Communications
Technical Papers
TimeWednesday, 7 December 20223:30pm - 5:00pm KST
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