人脸形状模型的多视图神经回归

中文摘要 AI 辅助

创建重拓扑的三维人脸网格对于高质量人脸动画至关重要，但仍费力且耗时。本论文探索更高效的方法来生成可用于生产的人脸网格，具体包括：（1）开发VarIS，一种定制的轻型球体，用于捕获高分辨率立体几何和反射率图；（2）分析影响自动二维和三维关键点标注的相机参数；（3）用于训练神经人脸回归的合成数据方法；（4）改进神经多视图人脸形状回归的技术。VarIS可实现逼真的人脸捕获，但其操作和处理成本促使人们寻求更具可扩展性的方法。因此，本文提出一种深度学习框架，直接从使用Visage Craft生成的合成多视图图像预测重拓扑人脸网格，Visage Craft是一款内部基于物理的渲染系统，使用外观三维可变形模型（A3DMM）。该系统在极少人工监督下生成可用于绑定和动画的标准化网格。结果表明，纳入准确的相机内参和外参可提升关键点精度和几何一致性，而三维关键点正则化进一步提升了重建质量。

英文摘要

Creating re-topologized 3D facial meshes is essential for high-quality facial animation but remains labor-intensive and time-consuming. This dissertation explores more efficient approaches for capturing production-ready facial meshes through: (1) the development of VarIS, a custom light sphere for capturing high-resolution stereo geometry and reflectance maps; (2) analysis of camera parameters affecting automatic 2D and 3D landmarking; (3) synthetic-data methods for training neural face regression; and (4) techniques for improving neural multi-view face-shape regression. While VarIS enables photorealistic face capture, its operational and processing costs motivate a more scalable approach. A deep learning framework is therefore proposed to directly predict re-topologized facial meshes from synthetic multiview images generated with Visage Craft, an in-house physically based rendering system using an Appearance 3D Morphable Model (A3DMM). The system produces standardized meshes ready for rigging and animation with minimal human supervision. Results show that incorporating accurate camera intrinsics and extrinsics improves landmark accuracy and geometric consistency, while 3D landmark regularization further improves reconstruction quality.

发表机构

• School of Computing（计算机学院）

机构由 AI 辅助整理，请以论文原文为准。