从最小的数据构建3D生成模型
Skylar Sutherland1,2, Bernhard Egger2,3, Joshua Tenenbaum2
1Department of Psychology, Yale University, 1 Hillhouse Ave, New Haven, CT 06511 USA.
概括
我们开发了一种新方法,可以从单次扫描中创建3D对象模型,并通过2D图像的无监督学习来增强它们. 这种方法可以从有限的数据中实现准确的3D面部识别和重建.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 3D 图形 3D 图形
背景情况:
- 传统的3D可变形模型 (3DMMs) 需要多次高质量的3D扫描.
- 现有的3D重建方法通常依赖于广泛的数据集.
- 从二维图像中推断出3D结构是一个重大挑战.
研究的目的:
- 提出一种用于从单个3D网格构建生成性3D对象模型的新方法.
- 从2D图像中使用无监督低射线学习来增强这些模型.
- 展示该方法在3D面部建模和识别领域的应用.
主要方法:
- 从单个3D扫描或模板中使用高斯过程构建3D可变形模型 (3DMMs),表示形状和白度.
- 使用无监督的低射线学习,使用少量2D图像来完善3DMM.
- 使用生成模型进行反向图形 (3D从2D重建) 和注册 (3D从3D).
主要成果:
- 从一个单一的3D模板成功生成了3D可变形模型.
- 通过每人只使用一个3D模板,证明了准确的3D面部识别.
- 展示了从二维图像中推断3D重建的能力.
- 开发了一个初步的无监督学习框架,用于3D面部分布学习.
结论:
- 拟议的方法可以从最小的数据创建强大的3D生成模型.
- 这种方法为使用有限的扫描进行3D面部识别和重建提供了可行的解决方案.
- 该方法为了解婴儿面部感知发展提供了一个潜在的计算模型.
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