Abstract | ||
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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.
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Year | DOI | Venue |
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2018 | 10.1145/3301506.3301519 | Proceedings of the 2018 the 2nd International Conference on Video and Image Processing |
Keywords | Field | DocType |
3D face reconstruction, CNN, low-resolution | Computer vision,3d geometry,Computer science,Convolutional neural network,Pixel,Artificial intelligence,Detector | Conference |
ISBN | Citations | PageRank |
978-1-4503-6613-7 | 0 | 0.34 |
References | Authors | |
0 | 4 |
Name | Order | Citations | PageRank |
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Rouven Winkler | 1 | 0 | 0.34 |
Chengchao Qu | 2 | 34 | 5.89 |
Sascha Voth | 3 | 18 | 2.58 |
Jürgen Beyerer | 4 | 315 | 75.37 |