"TED-Face":在野外的纹理增强深层面部重建
Ying Huang1, Lin Fang1, Shanfeng Hu2
1Institute of Virtual Reality and Intelligent Systems, Hangzhou Normal University, Hangzhou 311121, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
我们介绍TED-Face,这是一种从单个图像中进行3D面部重建的新方法. 它增强了面部几何和纹理细节,提高了视觉现实主义,超越了以前的最先进的方法.
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面部重建是具有挑战性的,因为对细节的感知敏感性.
- 现有的方法往往会丢失高频纹理信息,导致低保真度近似值.
- 在低维空间中最大限度地减少重建错误可能会导致在感知上微不足道但在数值上微小的错误.
研究的目的:
- 从单个图像开发一个高保真度的3D面部重建方法.
- 为了增强面部详细几何和外观的恢复,包括纹理.
- 为了解决当前重建技术中高频纹理细节的损失.
主要方法:
- 介绍了TED-Face,这是一个统一的深度神经网络,用于估计形状,外观,照明,摄像头,密集的辐射残留物和稀疏的面部纹理特征.
- 在多个流行的面部重建数据集上训练了网络.
- 开发了新的指标:视觉真实性 (VIF) 和结构相似性 (SSIM) 以更好地评估感知质量.
主要成果:
- 在FaceWarehouse基准指标上取得了最先进的表现,VIF得分为0.4802和SSIM得分为0.9622.
- 与Deep3D相比,LS3D-300W数据集对室内和室外图像的VIF和SSIM得分得到了改善.
- 该方法成功地恢复了视觉上更现实的面部外观细节.
结论:
- TED-Face显著提高了从单个图像中3D面部重建的质量.
- 提出的方法有效地恢复高保真度几何和增强纹理.
- 新的指标 (VIF和SSIM) 可以更好地评估面部重建中的感知现实性.
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