3D Face Reconstruction by Learning from Synthetic Data
Elad Richardson,Matan Sela,Ron Kimmel +2 more
- pp 460-469
TLDR
In this article, a CNN-based approach is proposed for reconstructing a 3D face from a single image, which is based on a convolutional-neural-network (CNN) architecture.Abstract:
Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications. Here, we introduce a learning-based approach for reconstructing a three-dimensional face from a single image. Recent face recovery methods rely on accurate localization of key characteristic points. In contrast, the proposed approach is based on a Convolutional-Neural-Network (CNN) which extracts the face geometry directly from its image. Although such deep architectures outperform other models in complex computer vision problems, training them properly requires a large dataset of annotated examples. In the case of three-dimensional faces, currently, there are no large volume data sets, while acquiring such big-data is a tedious task. As an alternative, we propose to generate random, yet nearly photo-realistic, facial images for which the geometric form is known. The suggested model successfully recovers facial shapes from real images, even for faces with extreme expressions and under various lighting conditions.read more
Citations
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Proceedings ArticleDOI
RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild
TL;DR: A novel single-shot, multi-level face localisation method, named RetinaFace, which unifies face box prediction, 2D facial landmark localisation and 3D vertices regression under one common target: point regression on the image plane.
Journal ArticleDOI
Deep video portraits
Hyeongwoo Kim,Pablo Garrido,Ayush Tewari,Weipeng Xu,Justus Thies,Matthias Niessner,Patrick Pérez,Christian Richardt,Michael Zollhöfer,Christian Theobalt +9 more
TL;DR: In this paper, a generative neural network with a novel space-time architecture is proposed to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor.
Book ChapterDOI
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
TL;DR: Yadira et al. as mentioned in this paper proposed a simple convolutional neural network to regress the 3D shape of a complete face from a single 2D image, which can reconstruct full facial geometry along with semantic meaning.
Proceedings ArticleDOI
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network
TL;DR: This paper used a CNN to regress 3DMM shape and texture parameters directly from an input photo and achieved state-of-the-art results on the LFW, YTF and IJB-A benchmarks.
Journal ArticleDOI
Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
TL;DR: A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings ArticleDOI
A morphable model for the synthesis of 3D faces
Volker Blanz,Thomas Vetter +1 more
TL;DR: A new technique for modeling textured 3D faces by transforming the shape and texture of the examples into a vector space representation, which regulates the naturalness of modeled faces avoiding faces with an “unlikely” appearance.
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Illumination for computer generated pictures
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