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  4. 3D Face Reconstruction from Low-Resolution Images with Convolutional Neural Networks
 
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2019
Conference Paper
Titel

3D Face Reconstruction from Low-Resolution Images with Convolutional Neural Networks

Abstract
During the past years, convolutional neural networks (CNNs) have widely spread as a powerful tool for tackling a variety of challenges posed in computer vision. Consequently, the trend neither does stop at 3D face reconstruction: Recently, several CNN-based approaches for reconstructing the dense 3D geometry of a face from only a single image have been introduced. However, while all of these methods deal with 3D face reconstruction in the high-resolution (HR) case, reconstruction in low-resolution (LR) surveillance scenarios by means of CNNs has not received any attention so far. With this work, we address that gap, being the first to propose a CNN architecture specifically tailored to LR 3D face reconstruction: We introduce an end-to-end trainable CNN capable of simultaneously estimating 3D geometry and pose of a face given a single LR image. By coupling our network with a state-of-the-art LR face detector, we build a 3D face reconstruction pipeline ready for integration into real-world applications. We conduct systematic evaluation on LR versions of the in-the-wild AFLW2000-3D dataset, considering decreasing interocular distances (IODs) down to three pixels. The results show superior performance of the proposed method in the LR domain over state-of-the-art approaches, for both 3D face reconstruction and the closely related face alignment task.
Author(s)
Winkler, Rouven
Qu, Chengchao
Voth, Sascha
Beyerer, Jürgen
Hauptwerk
ICVIP 2018, 2nd International Conference on Video and Image Processing. Proceedings
Konferenz
International Conference on Video and Image Processing (ICVIP) 2018
Thumbnail Image
DOI
10.1145/3301506.3301519
Language
English
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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
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