We propose Progressive Structure-conditional Generative Adversarial Networks (PSGAN), a new framework that can generate full-body and high-resolution character images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resolution images. However, existing approaches have limitations in achieving both high image quality and structural consistency at the same time. Our method tackles the limitations by progressively increasing the resolution of both generated images and structural conditions during training. In this paper, we empirically demonstrate the effectiveness of this method by showing the comparison with existing approaches and video generation results of diverse anime characters at 1024x1024 based on target pose sequences. We also create a novel dataset containing full-body 1024x1024 high-resolution images and exact 2D pose keypoints using Unity 3D Avatar models.
@article{arxiv.1809.01890,
title = {Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks},
author = {Koichi Hamada and Kentaro Tachibana and Tianqi Li and Hiroto Honda and Yusuke Uchida},
journal= {arXiv preprint arXiv:1809.01890},
year = {2018}
}
Comments
Accepted to ECCV 2018 Workshop: Computer Vision for Fashion, Art and Design. Project page is at https://dena.com/intl/anime-generation