Learning Dense Facial Correspondences in Unconstrained Images

ICCV, pp. 4733-4742, 2017.

Cited by: 25|Bibtex|Views28|DOI:https://doi.org/10.1109/iccv.2017.506
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

We present a minimalists but effective neural network that computes dense facial correspondences in highly unconstrained RGB images. Our network learns a per-pixel flow and a matchability mask between 2D input photographs of a person and the projection of a textured 3D face model. To train such a network, we generate a massive dataset of ...More

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