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Photorealistic Learned Landscapes for Augmented Reality

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Deep Portrait Image Completion and Extrapolation.

Xian Wu, Rui-Long Li, Fang-Lue Zhang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|October 16, 2019

Summary

This summary is machine-generated.

This study introduces a two-stage deep learning framework for realistic portrait image completion. The method accurately recovers human body structure and appearance, outperforming existing techniques.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

General image completion methods struggle with portrait images requiring human body structure and appearance synthesis.
Accurate recovery of incomplete human bodies in portraits is a challenging task.

Purpose of the Study:

To develop a novel two-stage deep learning framework for robust portrait image completion and extrapolation.
To improve the synthesis of human body structure and appearance in incomplete images.

Main Methods:

A two-stage framework utilizing a human parsing network with full-body pose estimation for structure recovery.
An image completion network trained with perceptual and conditional adversarial loss for realistic synthesis.
A face refinement network to enhance synthesized facial regions.

Main Results:

The proposed method outperforms state-of-the-art general image completion techniques on portrait datasets.
Demonstrated successful recovery of human body structure and realistic appearance synthesis.
The framework shows applicability to other image types, including animal images.

Conclusions:

The developed deep learning framework effectively addresses the challenges of portrait image completion.
Enables new applications like occlusion removal and portrait extrapolation.
The generalizable framework has potential for diverse image synthesis tasks.