A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision
Trainable videorealistic speech animation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Example Based 3D Reconstruction from Single 2D Images
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Make3D: Learning 3D Scene Structure from a Single Still Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphing-based analysis of the perceptual distance metric of human faces
Proceedings of the 7th Symposium on Applied Perception in Graphics and Visualization
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Manipulated versions of three-dimensional faces that have different profiles, but almost the same appearance in frontal views, provide a novel way to investigate if and how humans use class-specific knowledge to infer depth from images of faces. After seeing a frontal view, participants have to select the profile that matches that view. The profiles are original (ground truth), average, random other, and two solutions computed with a linear face model (3D Morphable Model). One solution is based on 2D vertex positions, the other on pixel colors in the frontal view. The human responses demonstrate that humans neither guess nor just choose the average profile. The results also indicate that humans actually use the information from the front view, and not just rely on the plausibility of the profiles per se. All our findings are perfectly consistent with a correlation-based inference in a linear face model. The results also verify that the 3D reconstructions from our computational algorithms (stimuli 4 and 5) are similar to what humans expect, because they are chosen to be the true profile equally often as the ground-truth profiles. Our experiments shed new light on the mechanisms of human face perception and present a new quality measure for 3D reconstruction algorithms.