论文标题
FFHQ-UV：3D脸重建
FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction
论文作者
论文摘要
我们提出了一个大规模的面部紫外线纹理数据集，其中包含50,000多个高质量的纹理紫外线图，并具有均匀的照明，中性表情和清洁的面部区域，这是在不同照明条件下呈现现实的3D面部模型的所需特征。该数据集借助我们的全自动且强大的UV-Textule Productine Prodine Prodine Prodine Prodine Prodine Prodipeline衍生自大规模面部图像数据集。我们的管道利用了基于样式的面部图像编辑方法的最新进展来生成单像输入的多视图归一化面图像。然后，应用详细的UV纹理提取，校正和完成程序，以从归一化的面部图像中产生高质量的紫外图。与现有的UV-Texture数据集相比，我们的数据集具有更多样化和更高质量的纹理图。我们进一步训练基于GAN的纹理解码器，作为基于参数拟合的3D面部重建的非线性纹理基础。实验表明，我们的方法提高了对最先进方法的重建精度，更重要的是，可以生成可用于现实效果的高质量纹理图。数据集，代码和预训练的纹理解码器可在https://github.com/csbhr/ffhq-uv上公开获得。
We present a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions. The dataset is derived from a large-scale face image dataset namely FFHQ, with the help of our fully automatic and robust UV-texture production pipeline. Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs. An elaborated UV-texture extraction, correction, and completion procedure is then applied to produce high-quality UV-maps from the normalized face images. Compared with existing UV-texture datasets, our dataset has more diverse and higher-quality texture maps. We further train a GAN-based texture decoder as the nonlinear texture basis for parametric fitting based 3D face reconstruction. Experiments show that our method improves the reconstruction accuracy over state-of-the-art approaches, and more importantly, produces high-quality texture maps that are ready for realistic renderings. The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-UV.
