Towards High-Fidelity Face Normal Estimation

While existing face normal estimation methods have produced promising results on small datasets, they often suffer from severe performance degradation on diverse in-the-wild face images, especially for the high-fidelity face normal estimation. Training a high-fidelity face normal estimation model with generalization capability requires a large amount of training data with face normal ground truth. Since collecting such high-fidelity database is difficult in practice, which prevents current methods from recovering face normal with fine-grained geometric details. To mitigate this issue, we propose a coarse-to-fine framework to estimate face normal from an in-the-wild image with only a coarse exemplar reference. Specifically, we first train a model using limited training data to exploit the coarse normal of a real face image. Then, we leverage the estimated coarse normal as an exemplar and devise an exemplar-based normal estimation network to explore robust mapping from the input face image to the fine-grained normal. In this manner, our method can largely alleviate the negative impact caused by lacking training data, and focus on exploring the high-fidelity normal contained in natural images. Extensive experiments and ablation studies are conducted to demonstrate the efficacy of our design, and reveal its superiority over state-of-the-art methods in terms of both training data requirement and recovery quality of fine-grained face normal. Our code is available at ://github.com/AutoHDR/HFFNE.