Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
@article{Jackson2017LargeP3, title={Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression}, author={Aaron S. Jackson and Adrian Bulat and Vasileios Argyriou and Georgios Tzimiropoulos}, journal={2017 IEEE International Conference on Computer Vision (ICCV)}, year={2017}, pages={1031-1039} }
3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. [] Key Method We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality, especially for the cases of large poses and facial expressions. Code and models will…
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References
SHOWING 1-10 OF 32 REFERENCES
Learning Detailed Face Reconstruction from a Single Image
- Computer Science2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2017
This work proposes to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image, and introduces an end- to-end CNN framework which derives the shape in a coarse-to-fine fashion.
Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting
- Computer Science2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2016
This paper proposes a face alignment method for large-pose face images, by combining the powerful cascaded CNN regressor method and 3DMM, and forms the face alignment as a3DMM fitting problem, where the camera projection matrix and3D shape parameters are estimated by a cascade of CNN-based regressors.
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network
- Computer Science2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2017
A robust method for regressing discriminative 3D morphable face models (3DMM) using a convolutional neural network to regress 3DMM shape and texture parameters directly from an input photo is described.
Face Alignment Across Large Poses: A 3D Solution
- Computer Science2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2016
3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN), is proposed, and a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling is proposed.
Face reconstruction in the wild
- Environmental Science2011 International Conference on Computer Vision
- 2011
This work addresses the problem of reconstructing 3D face models from large unstructured photo collections, e.g., obtained by Google image search or from personal photo collections in iPhoto, and leverages multi-image shading, but unlike traditional photometric stereo approaches, allows for changes in viewpoint and shape.
3D Face Reconstruction from a Single Image Using a Single Reference Face Shape
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2011
This work proposes a novel method for 3D shape recovery of faces that exploits the similarity of faces, and obtains as input a single image and uses a mere single 3D reference model of a different person's face.
Joint Face Alignment and 3D Face Reconstruction
- Computer ScienceECCV
- 2016
The proposed method iteratively and alternately applies two sets of cascaded regressors, one for updating 2D landmarks and the other for updating reconstructed pose-expression-normalized (PEN) 3D face shape, to simultaneously solve the two problems of face alignment and3D face reconstruction from an input 2D face image of arbitrary poses and expressions.
A 3D Face Model for Pose and Illumination Invariant Face Recognition
- Computer Science2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
- 2009
This paper publishes a generative 3D shape and texture model, the Basel Face Model (BFM), and demonstrates its application to several face recognition task and publishes a set of detailed recognition and reconstruction results on standard databases to allow complete algorithm comparisons.
A Multiresolution 3D Morphable Face Model and Fitting Framework
- Computer ScienceVISIGRAPP
- 2016
The Surrey Face Model is presented, a multi-resolution 3D Morphable Model that is made available to the public for non-commercial purposes and a lightweight open-source C++ library designed with simplicity and ease of integration as its foremost goals.
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
- Computer Science2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2017
This paper proposes a fine discretization of the 3D space around the subject and trains a ConvNet to predict per voxel likelihoods for each joint, which creates a natural representation for 3D pose and greatly improves performance over the direct regression of joint coordinates.