Generative Face Completion

Published in

Computer Vision and Pattern Recognition(2017)

External Links:

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TL;DR

This paper demonstrates qualitatively and quantitatively that the proposed effective face completion algorithm is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results.

Abstract

In this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g., eyes and mouths) that contain large appearance variations. Unlike existing nonparametric algorithms that search for patches to synthesize, our algorithm directly generates contents for missing regions based on a neural network. The model is trained with a combination of a reconstruction loss, two adversarial losses and a semantic parsing loss, which ensures pixel faithfulness and local-global contents consistency. With extensive experimental results, we demonstrate qualitatively and quantitatively that our model is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results.

Authors

Ming-Hsuan Yang

12 papers

Jimei Yang

14 papers

Yijun Li

2 papers

References29 items

Context Encoders: Feature Learning by Inpainting

Generative Image Modeling Using Style and Structure Adversarial Networks

Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

Generating Images with Perceptual Similarity Metrics based on Deep Networks

Pixel Recurrent Neural Networks

Generative Face Completion

Published in

Computer Vision and Pattern Recognition(2017)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Ming-Hsuan Yang

12 papers

Jimei Yang

14 papers

Yijun Li

2 papers

References29 items

Context Encoders: Feature Learning by Inpainting

Generative Image Modeling Using Style and Structure Adversarial Networks

Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

Generating Images with Perceptual Similarity Metrics based on Deep Networks

Pixel Recurrent Neural Networks

Autoencoding beyond pixels using a learned similarity metric

Shepard Convolutional Neural Networks

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Inverting Visual Representations with Convolutional Networks

Multi-objective convolutional learning for face labeling

Deep Learning Face Attributes in the Wild

Fully convolutional networks for semantic segmentation

Very Deep Convolutional Networks for Large-Scale Image Recognition

Image completion using planar structure guidance

Auto-Encoding Variational Bayes

Exemplar-Based Face Parsing

Interactive Facial Feature Localization

From learning models of natural image patches to whole image restoration

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

PatchMatch: a randomized correspondence algorithm for structural image editing

Curriculum learning

Robust Face Recognition via Sparse Representation

Extracting and composing robust features with denoising autoencoders

Scene Completion Using Millions of Photographs

Poisson image editing

Simultaneous structure and texture image inpainting

GENERATIVE ADVERSARIAL NETS

OpenFace: A general-purpose face recognition library with mobile applications

Field of Study

Computer ScienceMathematics

Journal Information

Name

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Page

1800-1807

Venue Information

Name

Computer Vision and Pattern Recognition

Type

conference

URL

https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147

Alternate Names

CVPR
Comput Vis Pattern Recognit

TL;DR

Abstract

Authors

References29 items

Context Encoders: Feature Learning by Inpainting

Generative Image Modeling Using Style and Structure Adversarial Networks

Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

Generating Images with Perceptual Similarity Metrics based on Deep Networks

Pixel Recurrent Neural Networks

TL;DR

Abstract

Authors

References29 items

Context Encoders: Feature Learning by Inpainting

Generative Image Modeling Using Style and Structure Adversarial Networks

Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

Generating Images with Perceptual Similarity Metrics based on Deep Networks

Pixel Recurrent Neural Networks

Autoencoding beyond pixels using a learned similarity metric

Shepard Convolutional Neural Networks

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Inverting Visual Representations with Convolutional Networks

Multi-objective convolutional learning for face labeling

Deep Learning Face Attributes in the Wild

Fully convolutional networks for semantic segmentation

Very Deep Convolutional Networks for Large-Scale Image Recognition

Image completion using planar structure guidance

Auto-Encoding Variational Bayes

Exemplar-Based Face Parsing

Interactive Facial Feature Localization

From learning models of natural image patches to whole image restoration

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

PatchMatch: a randomized correspondence algorithm for structural image editing

Curriculum learning

Robust Face Recognition via Sparse Representation

Extracting and composing robust features with denoising autoencoders

Scene Completion Using Millions of Photographs

Poisson image editing

Simultaneous structure and texture image inpainting

Image inpainting

GENERATIVE ADVERSARIAL NETS

OpenFace: A general-purpose face recognition library with mobile applications

Field of Study

Journal Information

Name

Page

Venue Information

Name

Type

URL

Alternate Names