不平等受约束的3D形态面部模型适配不平等受约束的3D形态面部模型适配
IEEE transactions on pattern analysis and machine intelligence
|November 28, 2023
概括
本研究介绍了不平等受约束的3D形态模型 (3DMM) 拟合,为准确的3D面部形状估计提供了传统规范化的强大替代方案. 这种新的方法增强了对个人的具体细节捕获,并在不受约束的环境中有效工作.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 三维重建的3D重建
背景情况:
- 传统的3D形态模型 (3DMM) 配件依赖于不受约束的优化,以合理的面部形状和地标一致性进行规范化.
- 规范化方法可能会限制捕捉细微,个体特有的面部细节的能力.
研究的目的:
- 将不平等受约束的3DMM配件作为3D面部重建的新,没有规范化的替代方案.
- 提高3DMM配件的准确性,稳定性和效率,而不需要基于学习的方法.
主要方法:
- 对3DMM形状系数实施不平等约束,以确保类似面部的形状.
- 在2D地标上应用不平等约束,以提高无需每图像调整的稳定性.
- 利用日志屏障方法来有效地实现受约束优化框架.
主要成果:
- 拟议的方法通过将3DMM连接到多个来准确估计个体特定的面部形状.
- 通过渐变相关性,即使使用受控数据,也证明了"在野外"3DMM安装的稳定性.
- 在没有任何学习组件的情况下实现了准确,强大和高效的3DMM配件.
结论:
- 不平等受约束的3DMM配件为3D面部重建提供了一个灵活和有效的替代规范化的替代方案.
- 该框架是通用的,支持可变输入大小,可互换模型和相机矩阵集成.
- 这种无学习的方法在准确和强大的3D面部形状分析方面取得了重大进展.
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