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. 2022 Sep 15;28(9):1883–1892. doi: 10.1038/s41591-022-01966-1

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — Our approach uses RGB images as input and outputs 3D reconstructed meshes. For a particular frame, the algorithm first extracts 2D face landmarks from the RGB image and fits a set of face model weights for 3D face reconstruction. Then, the algorithm extracts 2D eyelid landmarks and 2D semantic lines and fits eyelid model weights for 3D eyelid reconstruction. Finally, the algorithm extracts 2D iris landmarks and solves eyeball rotation for 3D eyeball reconstruction.