用GAN生成多视角人脸，结合3D建模实现高精度纹理重建。
3D-GANTex: 3D Face Reconstruction with StyleGAN3-based Multi-View Images and 3DDFA based Mesh Generation
- 基于StyleGAN的潜在空间生成多视角图像，增强信息量。
- 3DDFA模型从单图推断出高质量3D网格与高分辨率纹理图。
- 适合需要高保真3D人脸重建的应用，如虚拟形象、影视特效。
从单张人脸图像中估计几何与纹理是一个病态问题，尤其当人脸存在不同角度旋转时更为严峻。本文提出一种新方法，首先利用StyleGAN和3D可变形模型（3DMM）生成多视角人脸图像以丰富输入信息；随后，使用在3DMM上训练的3DDFA模型估计出3D人脸网格及与形状一致的高分辨率纹理图。实验表明，所生成的3D网格质量高，纹理还原接近真实水平，有效缓解了单视角重建的模糊性问题。
原文摘要 · Abstract (English)
Geometry and texture estimation from a single face image is an ill-posed problem since there is very little information to work with. The problem further escalates when the face is rotated at a different angle. This paper tries to tackle this problem by introducing a novel method for texture estimation from a single image by first using StyleGAN and 3D Morphable Models. The method begins by generating multi-view faces using the latent space of GAN. Then 3DDFA trained on 3DMM estimates a 3D face mesh as well as a high-resolution texture map that is consistent with the estimated face shape. The result shows that the generated mesh is of high quality with near to accurate texture representation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。