Text2Face:基于文本的面部生成与几何和外观控制
IEEE transactions on visualization and computer graphics
|January 2, 2024
概括
本研究引入了一种使用图形神经网络的新框架,以减少面对面文本生成中的模两可. 该方法有效地从文本描述中创建高质量,属性条件的面部图像.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于文本的人脸生成旨在弥合文本和视觉之间的语义差距.
- 由于文本和面部图像之间的许多对许多映射,现有的方法面临着模两可.
- 面部部位属性 (几何和外观) 呈现出不符合均分布的相互依赖性.
研究的目的:
- 为面对面的文本生成开发一个强大的框架,以减轻模两可.
- 为了更准确的图像合成,模拟面部部位之间的相互依赖性.
- 从文本描述中生成高质量的,属性条件化的面部图像.
主要方法:
- 引入了一个局部到全球的框架,包含两个图形神经网络 (GNN).
- 一个GNN模型面部几何学,而另一个模型面部外观.
- 借助数据集分布学习和部分描述,为改进文本对面映射提供建议.
主要成果:
- 拟议的框架有效地模拟了面部部件之间的相互依赖.
- 从文本输入生成高质量的属性条件面部图像.
- 与现有的面对面文本生成方法相比,其表现优越的性能和可用性.
结论:
- 嵌入式GNN的本地到全球框架成功解决了面对面的文本生成中的模两可.
- 该方法从数据分布中学习的能力增强了面部图像合成.
- 这种方法在从文本提示生成真实的面部图像方面取得了重大进展.
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