一个分层的多分辨率自我监督的框架,用于高保真度的3D面部重建,使用可学习的Gabor-Aware纹理建模
Pichet Mareo1, Rerkchai Fooprateepsiri2
1Business Administration and Information Technology Faculty, Rajamangala University of Technology Tawan-ok, Bangkok 10400, Thailand.
Journal of imaging
|January 27, 2026
概括
本研究介绍了3D面部重建的层次框架,从单个图像中提高了细节精度. 该方法增强了几何和纹理的真实性,为复杂的面部数据提供了强大的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 机器学习 机器学习
背景情况:
- 从单个图像进行3D面部重建是很困难的,因为深度模糊,纹理纠.
- 现有的方法在细微细节和对变化的稳定性方面扎.
研究的目的:
- 提出一个分层的多分辨率自我监督框架 (HMR-Framework),用于高保真度的3D面部重建.
- 在一个统一的管道内逐步重建粗,中和细尺度的面部几何.
- 为了增强精细尺度的纹理保真性,并保持边缘感知特征.
主要方法:
- 层次的多个解决方案自我监督框架 (HMR框架).
- 通过3D形态模型回归进行粗的几何预估.
- 使用顶点变形地图与全球-本地马尔科夫随机场损失进行中等规模的精细化.
- 可学习的Gabor感知质感增强模块用于空间频率脱.
- 基于波形的细节感知损失,用于边缘感知纹理保护.
主要成果:
- 与最先进的方法相比,HMR框架实现了优越的细节重建.
- 该框架在各种姿势变化中表现出强度.
- 层次设计增强了几何尺度上的语义一致性.
结论:
- 拟议的HMR-Framework提供了一个功能解决方案,用于从单眼图像中高准确度的3D面部重建.
- 该方法有效地解决了深度模糊性和多尺度纹理纠的挑战.
- 层次化的方法提高了3D面部建模的准确性和稳定性.
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