Learning to hallucinate face images via component generation and enhancement

Learning to hallucinate face images via component generation and enhancement

We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures...

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Bibliographic Details
Main Authors: SONG, Yibing, ZHANG, Jiawei, HE, Shengfeng, BAO, Linchao, YANG, Qingxiong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Artificial intelligence
Tunneling (excavation)
Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
Online Access:https://ink.library.smu.edu.sg/sis_research/8429
https://ink.library.smu.edu.sg/context/sis_research/article/9432/viewcontent/0633.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods.

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