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Related Experiment Video

Updated: Oct 6, 2025

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

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Person image generation through graph-based and appearance-decomposed generative adversarial network.

Yuling He¹, Yingding Zhao², Wenji Yang²

¹School of Computer and Information Engineering, Jiangxi Agricultural University, NanChang, JiangXi, China.

Peerj. Computer Science

|January 17, 2022

Summary

This summary is machine-generated.

This study introduces a new framework for generating realistic person images with consistent shape and appearance. It uses graph networks and attribute decomposition for improved pose transfer and image quality.

Keywords:

Generative adversarial network Graph network Image generation Pose transfer

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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

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Published on: June 3, 2013

Area of Science:

Computer Vision
Artificial Intelligence
Image Generation

Background:

Generating person images with non-rigid deformations across different poses is complex.
Existing methods struggle with maintaining shape and appearance consistency during pose transfer.

Purpose of the Study:

To develop a novel framework for generating person images with consistent shape and appearance.
To improve the realism and quality of generated person images through advanced pose transfer techniques.

Main Methods:

Leveraging graph networks to infer global pose relationships for accurate pose transfer.
Decomposing source images into attributes (hair, clothes, pants, shoes) and combining them with pose coding.
Employing an alternate updating strategy for mutual guidance between pose and appearance modules.

Main Results:

The framework successfully generates person images with high shape and appearance consistency.
Experiments on the DeepFashion dataset demonstrate the effectiveness of the proposed approach.
The method achieves improved realism compared to existing pose transfer techniques.

Conclusions:

The novel framework effectively addresses the challenges of non-rigid deformation in person image generation.
The combination of graph networks, attribute decomposition, and alternate updating significantly enhances image quality.
The proposed method offers a promising solution for realistic and consistent person image synthesis.