FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction

TL;DR

This paper introduces a large-scale, high-quality facial UV-texture dataset derived from FFHQ, enabling improved 3D face reconstruction and realistic rendering through a novel pipeline and a GAN-based texture decoder.

Contribution

The authors present a new diverse UV-texture dataset with an automatic pipeline for high-quality texture map generation and a GAN-based decoder that enhances 3D face reconstruction accuracy.

Findings

Improved 3D face reconstruction accuracy over state-of-the-art methods.

Produced high-quality, realistic facial textures suitable for rendering.

Dataset and tools are publicly available for research use.

Abstract

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.