用GAN补全缺失姿态的像素角色图，减轻动画制作重复劳动。
A Missing Data Imputation GAN for Character Sprite Generation
- 将角色生成视为缺失数据填补问题，输入已知视角图像生成目标姿态。
- 在缺1~3张图时表现优于或接近现有最优方法。
- 适合游戏/动画从业者快速生成角色多角度像素图。
生成和更新包含多个帧、不同动作与姿势的像素艺术角色图耗时且易重复。本文提出一种新方法，通过将角色生成问题建模为缺失数据填补任务，利用生成对抗网络（GAN）从角色已知的三个方向图像中生成目标姿态的像素图。模型在存在1、2或3张缺失图像的情况下进行评估，当可用图像较多时，性能达到或超过当前最优水平。同时，我们还研究了对基础架构的改进对结果的影响。
原文摘要 · Abstract (English)
Creating and updating pixel art character sprites with many frames spanning different animations and poses takes time and can quickly become repetitive. However, that can be partially automated to allow artists to focus on more creative tasks. In this work, we concentrate on creating pixel art character sprites in a target pose from images of them facing other three directions. We present a novel approach to character generation by framing the problem as a missing data imputation task. Our proposed generative adversarial networks model receives the images of a character in all available domains and produces the image of the missing pose. We evaluated our approach in the scenarios with one, two, and three missing images, achieving similar or better results to the state-of-the-art when more images are available. We also evaluate the impact of the proposed changes to the base architecture.
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