Abstract
In this paper we take a fresh look at the problem of extracting shape from contours of human faces. We focus on two key questions: how can we robustly fit a 3D face model to a given input contour; and, how much information about shape does a single contour image convey.
Our system matches silhouettes and inner contours of a PCA based Morphable Model to an input contour image. We discuss different types of contours in terms of their effect on the continuity and differentiability of related error functions and justify our choices of error function (modified Euclidean Distance Transform) and optimization algorithm (Downhill Simplex).
In a synthetic test setting we explore the limits of accuracy when recovering shape and pose from a single correct input contour and find that pose is much better captured by contours than is shape. In a semi-synthetic test setting – the input images are edges extracted from photorealistic renderings of the PCA model – we investigate the robustness of our method and argue that not all discrepancies between edges and contours can be solved by the fitting process alone.
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Keller, M., Knothe, R., Vetter, T. (2007). 3D Reconstruction of Human Faces from Occluding Contours. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2007. Lecture Notes in Computer Science, vol 4418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71457-6_24
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DOI: https://doi.org/10.1007/978-3-540-71457-6_24
Publisher Name: Springer, Berlin, Heidelberg
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