2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022
DOI: 10.1109/cvpr52688.2022.00158
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NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations

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Cited by 13 publications
(18 citation statements)
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“…Inspired by the advances of learning-based shape recon-struction, 3D strand models are generated by neural networks as explicit point sequences [46], volumetric orientation field [26,30,41], and implicit orientation field [37] from single-view input. With the above evolution of 3D hair representations, the quality of recovered shape has been improved significantly.…”
Section: Introductionmentioning
confidence: 99%
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“…Inspired by the advances of learning-based shape recon-struction, 3D strand models are generated by neural networks as explicit point sequences [46], volumetric orientation field [26,30,41], and implicit orientation field [37] from single-view input. With the above evolution of 3D hair representations, the quality of recovered shape has been improved significantly.…”
Section: Introductionmentioning
confidence: 99%
“…With the above evolution of 3D hair representations, the quality of recovered shape has been improved significantly. As populating pairs of 3D hair and real images is challenging [46], existing learning-based methods [26,30,37,40,46] are just trained on synthetic data before applying on real portraits. However, the domain gap between rendered images (from synthetic hair models) and real images has a great and negative impact on the quality of reconstructed results.…”
Section: Introductionmentioning
confidence: 99%
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