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.

提出了一种新的Progressive Structure-conditional Generative Adversarial Networks (PSGAN)框架，可以根据结构信息生成全身和高分辨率的角色图像，并通过比较现有方法及生成高分辨率图像和结构条件的分辨率，以实际结果证明该方法的有效性。

使用渐进式结构条件生成对抗网络进行全身高分辨率动漫生成