Kerbiriou, GlennAvril, QuentinMarchal, MaudBermano, Amit H.Kalogerakis, Evangelos2024-04-302024-04-3020241467-8659https://doi.org/10.1111/cgf.15040https://diglib.eg.org/handle/10.1111/cgf15040High-fidelity digital human modeling has become crucial in various applications, including gaming, visual effects and virtual reality. Despite the significant impact of eyelashes on facial aesthetics, their reconstruction and modeling have been largely unexplored. In this paper, we introduce the first data-driven generative model of eyelashes based on semantic features. This model is derived from real data by introducing a new 3D eyelash reconstruction method based on multi-view images. The reconstructed data is made available which constitutes the first dataset of 3D eyelashes ever published. Through an innovative extraction process, we determine the features of any set of eyelashes, and present detailed descriptive statistics of human eyelashes shapes. The proposed eyelashes model, which exclusively relies on semantic parameters, effectively captures the appearance of a set of eyelashes. Results show that the proposed model enables interactive, intuitive and realistic eyelashes modeling for non-experts, enriching avatar creation and synthetic data generation pipelines.Attribution-NonCommercial-NoDerivatives 4.0 International LicenseCCS Concepts: Modeling/Geometry -> Facial Modeling; Datasets/Evaluation/Perception; Hair ModelingModeling/GeometryFacial ModelingDatasets/Evaluation/PerceptionHair Modeling10.1111/cgf.1504017 pages