3D生成模型 通过局部 Eigenprojection 进行潜伏解.
Simone Foti1, Bongjin Koo1,2, Danail Stoyanov1
1University College London London UK.
概括
这项研究引入了一种新的方法,通过提高对生成模型中局部形状属性的控制来创建现实的数字人类. 这种新的方法增强了解,以便在3D网格生成中更好地控制属性.
科学领域:
- 计算机图形 计算机图形
- 人工智能的人工智能
- 计算几何学的计算几何学
背景情况:
- 创建现实的数字人类是具有挑战性的.
- 当前的生成模型缺乏对局部形状属性的控制.
- 现有的方法在3D网格生成中扎着属性解.
研究的目的:
- 引入一种新的损失函数,以便在数字人类创作中更好地控制局部形状属性.
- 为了增强3D头部和身体网格的生成模型中的解.
- 为了在3D网格生成中更好地解属性创建.
主要方法:
- 开发了一种基于光谱几何学的新浪损失函数.
- 将损失函数应用于基于神经网络的生成模型,如变化自编码器 (VAE) 和生成对抗网络 (GAN).
- 鼓励潜在变量遵循身份属性的本地特征投影,以改善解.
主要成果:
- 拟议的本地自投射解 (LED) 模型与最先进的方法相比,显示出更高的解能力.
- 保持了强大的生成能力,同时改善了属性控制.
- 取得的培训时间与标准的VAE和GAN实现相美.
结论:
- 这种LED方法有效地解决了3D网格生成模型中局部形状属性控制的局限性.
- 基于光谱几何学的损失函数为解散的表示学习提供了一个有希望的方向.
- 这项工作推动了对几何细节进行增强控制的现实数字人类的创造.
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