3D cartoon face generation by local deformation mapping

Zhou, Jingyong; Tong, Xin; Liu, Zicheng; Guo, Baining

doi:10.1007/s00371-016-1265-5

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…Li et al (2008) and Liu et al (2009) both focused on learning a mapping between the LLE representation of photographs and their corresponding LLE representation of 3D caricatures modeled by artists. In the same vein, but only in the 3D domain, Zhou et al (2016) regressed a set of locally linear mappings from sparse exemplars of 3D faces and their corresponding 3D caricature. As far as we know, Clarke et al (2011) are the only authors that proposed a physics-oriented caricature method.…”

Section: Learning Based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Study on Automatic 3D Facial Caricaturization: From Rules to Deep Learning

Olivier

Kerbiriou

Arguelaguet

et al. 2022

Front. Virtual Real.

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Facial caricature is the art of drawing faces in an exaggerated way to convey emotions such as humor or sarcasm. Automatic caricaturization has been explored both in the 2D and 3D domain. In this paper, we propose two novel approaches to automatically caricaturize input facial scans, filling gaps in the literature in terms of user-control, caricature style transfer, and exploring the use of deep learning for 3D mesh caricaturization. The first approach is a gradient-based differential deformation approach with data driven stylization. It is a combination of two deformation processes: facial curvature and proportions exaggeration. The second approach is a GAN for unpaired face-scan-to-3D-caricature translation. We leverage existing facial and caricature datasets, along with recent domain-to-domain translation methods and 3D convolutional operators, to learn to caricaturize 3D facial scans in an unsupervised way. To evaluate and compare these two novel approaches with the state of the art, we conducted the first user study of facial mesh caricaturization techniques, with 49 participants. It highlights the subjectivity of the caricature perception and the complementarity of the methods. Finally, we provide insights for automatically generating caricaturized 3D facial mesh.

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Section: Learning Based Methodsmentioning

confidence: 99%

“…In face modeling, cartoonization and caricaturing, semantic segmentation is a popular technique for increasing expressivity and user interaction (Blanz and Vetter, 1999;Liu et al, 2009;Zhou et al, 2016). In the proposed system, the 3D faces are segmented using the scheme proposed by Blanz and Vetter (1999) i.e.…”

Section: Facial Segmentationmentioning

confidence: 99%

Study on Automatic 3D Facial Caricaturization: From Rules to Deep Learning

Olivier

Kerbiriou

Arguelaguet

et al. 2022

Front. Virtual Real.

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“…Semantic deformation transfer [17] inferred a correspondence between the shape spaces of the two characters from given example mesh pairs by using standard linear algebra. Zhou et al [18] further utilized these methods to automatically generate a 3D cartoon of a real 3D face. Thies et al [2] developed a system that transfers expression changes from the source to the target actor based on [15] and achieves real-time performance.…”

Section: Deformation Transfermentioning

confidence: 99%

Cartoonish sketch-based face editing in videos using identity deformation transfer

Zhao

Han

Peng

et al. 2019

Computers & Graphics

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A B S T R A C TWe address the problem of using hand-drawn sketches to create exaggerated deformations to faces in videos, such as enlarging the shape or modifying the position of eyes or mouth. This task is formulated as a 3D face model reconstruction and deformation problem. We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame. At the same time, user's editing intention is recognized from input sketches as a set of facial modifications. Then a novel identity deformation algorithm is proposed to transfer these facial deformations from 2D space to the 3D facial identity directly while preserving the facial expressions. After an optional stage for further refining the 3D face model, these changes are propagated to the whole video with the modified identity. Both the user study and experimental results demonstrate that our sketching framework can help users effectively edit facial identities in videos, while high consistency and fidelity are ensured at the same time.

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“…The papers by Lin et al [1] and Giachetti et al [2] are about character body modelling. The two next papers by Huang et al [3] and Zhou et al [4] focus on virtual garment stretching and face generation for virtual characters. The papers by Bao et al [5] and Andersen et al [6] are about hair and fur simulation.…”

Section: Articles In This Issuementioning

confidence: 99%