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Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

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Unsupervised High-Resolution Portrait Gaze Correction and Animation.

Jichao Zhang, Jingjing Chen, Hao Tang

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

|July 27, 2022

Summary

This summary is machine-generated.

This study introduces an unsupervised method for gaze correction and animation in portrait images, eliminating the need for gaze angle and head pose annotations. The approach enhances realism in unconstrained, high-resolution face images.

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Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Gaze correction methods typically require precise gaze and head pose annotations.
Unsupervised solutions for gaze correction in high-resolution, unconstrained images remain challenging due to annotation difficulties.

Purpose of the Study:

To propose an unsupervised method for gaze correction and animation in high-resolution portrait images.
To develop a system that does not require gaze angle and head pose annotations for training.

Main Methods:

Creation of two new portrait datasets: CelebGaze (256x256) and CelebHQGaze (512x512).
Formulation of gaze correction as an image inpainting problem using a Gaze Correction Module (GCM) and Gaze Animation Module (GAM).
Proposal of an unsupervised 'Synthesis-As-Training' strategy and a Coarse-to-Fine Module (CFM) to reduce computational costs.

Main Results:

The method effectively performs gaze correction and animation on low and high-resolution face datasets.
Demonstrated superiority over existing state-of-the-art methods in unconstrained, in-the-wild scenarios.
Successful gaze animation achieved through semantic interpolation in a learned latent space.

Conclusions:

The proposed unsupervised method offers an effective solution for gaze correction and animation without manual annotations.
The integration of GCM, GAM, and CFM optimizes performance and efficiency for real-world applications.
This work advances the field of facial image manipulation, particularly for unconstrained, high-resolution portraits.