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dc.contributor.authorHuang, Meijiaen_US
dc.contributor.authorDai, Juen_US
dc.contributor.authorPan, Junjunen_US
dc.contributor.authorBai, Junxuanen_US
dc.contributor.authorQin, Hongen_US
dc.contributor.editorLee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkharden_US
dc.date.accessioned2021-10-14T10:05:43Z
dc.date.available2021-10-14T10:05:43Z
dc.date.issued2021
dc.identifier.isbn978-3-03868-162-5
dc.identifier.urihttps://doi.org/10.2312/pg.20211387
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pg20211387
dc.description.abstractCaricatures are an artistic representation of human faces to express satire and humor. Caricature generation of human faces is a hotspot in CG research. Previous work mainly focuses on 2D caricatures generation from face photos or 3D caricature reconstruction from caricature images. In this paper, we propose a novel end-to-end method to directly generate personalized 3D caricatures from a single natural face image. It can create not only exaggerated geometric shapes, but also heterogeneous texture styles. Firstly, we construct a synthetic dataset containing matched data pairs composed of face photos, caricature images, and 3D caricatures. Then, we design a graph convolutional autoencoder to build a non-linear colored mesh model to learn the shape and texture of 3D caricatures. To make the network end-to-end trainable, we incorporate a differentiable renderer to render 3D caricatures into caricature images inversely. Experiments demonstrate that our method can achieve 3D caricature generation with various texture styles from face images while maintaining personality characteristics.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectImage processing
dc.subjectMesh geometry models
dc.title3D-CariNet: End-to-end 3D Caricature Generation from Natural Face Images with Differentiable Rendereren_US
dc.description.seriesinformationPacific Graphics Short Papers, Posters, and Work-in-Progress Papers
dc.description.sectionheadersNeural Rendering and 3D Models
dc.identifier.doi10.2312/pg.20211387
dc.identifier.pages49-54


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