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

• URL: http://arxiv.org/abs/2211.13874v2
• Date: Fri, 24 Mar 2023 14:44:50 GMT
• Title: FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction
• Authors: Haoran Bai, Di Kang, Haoxian Zhang, Jinshan Pan, Linchao Bao
• Abstract summary: This dataset contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions. Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches. Experiments show that our method improves the reconstruction accuracy over state-of-the-art approaches.
• Score: 46.3392612457273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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