Realistic modeling for facial animation

Lee, Yuencheng; Terzopoulos, Demetri; Waters, Keith

doi:10.1145/218380.218407

Cited by 661 publications

(355 citation statements)

References 21 publications

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“…As to the generation of facial models with anatomical structure, multilayer generic template is used [8,9,10,11]. Skin, muscle layer and skull are represented as polygonal meshes [8,9] or as volumetric elements [10,11].…”

Section: Related Workmentioning

confidence: 99%

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Vision Based Speech Animation Transferring with Underlying Anatomical Structure

Pei

Zha

2006

Computer Vision – ACCV 2006

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Abstract. We present a novel method to transfer speech animation recorded in low resolution videos onto realistic 3D facial models. Unsupervised learning is utilized on a speech video corpus to find underlying manifold of facial configurations. K-means clustering is applied on the low dimensional space to find key speaking-related facial shapes. With a small set of laser scanner captured 3D models related to the clustering centroid, the facial animation in 2D videos is transferred onto 3D shapes. Especially by virtue of a weak perspective projection model, the underlying mandible rotation is recovered from videos and is utilized to drive 3D skull movements. The adaption of a generic skull onto facial models is guided by a 2D image, Tissue Map. With parsimonious data requirements, our system realizes the animation transferring and gains a realistic rendering effect with the underlying anatomical structure.

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Section: Related Workmentioning

confidence: 99%

“…Skin, muscle layer and skull are represented as polygonal meshes [8,9] or as volumetric elements [10,11]. The dynamical mechanism is simulated by the mass-spring system or finite elements analysis [10,11].…”

Section: Related Workmentioning

confidence: 99%

Vision Based Speech Animation Transferring with Underlying Anatomical Structure

Pei

Zha

2006

Computer Vision – ACCV 2006

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“…These models emphasized the simulation of muscle movements. They are relatively simple compared with the one developed by Lee et al [21]. Lee's model has a three-level structure of skin, bone and muscle, based on physiology.…”

Section: Introductionmentioning

confidence: 99%

Facial expression transfer method based on frequency analysis

Wei

Tian

Maybank

et al. 2016

Pattern Recognition

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Abstract. We propose a novel expression transfer method based on an analysis of the frequency of multi-expression facial images. We locate the facial features automatically and describe the shape deformations between a neutral expression and non-neutral expressions. The subtle expression changes are important visual clues to distinguish different expressions. These changes are more salient in the frequency domain than in the image domain. We extract the subtle local expression deformations for the source subject, coded in the wavelet decomposition. This information about expressions is transferred to a target subject. The resulting synthesized image preserves both the facial appearance of the target subject and the expression details of the source subject.This method is extended to dynamic expression transfer to allow a more precise interpretation of facial expressions.Experiments on Japanese Female Facial Expression (JAFFE), the extended Cohn-Kanade (CK+) and PIE facial expression databases show the superiority of our method over the state-of-the-art method.

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“…First, MSM was initially accepted for real-time face animation due to its well-known physics and computational efficiency. Terzopoulos and Waters (1990) and Lee et al (1995) proposed a fast simulation method using MSM with linear facial muscle model. Keeve et al (1998) presented a MSM with prismatic element, and compared the result with FEM in terms of accuracy and computational cost.…”

Section: Introductionmentioning

confidence: 99%

A new soft-tissue simulation strategy for cranio-maxillofacial surgery using facial muscle template model

Kim

Jürgens

Weber

et al. 2010

Progress in Biophysics and Molecular Biology

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We propose a computationally efficient, bio-mechanically relevant soft-tissue simulation method for cranio-maxillofacial (CMF) surgery. Special emphasis is given to comply with the current clinical workflow. A template-based facial muscle prediction was introduced to avoid laborious segmentation from medical images. In addition, transversely isotropic mass-tensor model (MTM) was applied to realize the directional behavior of facial muscles in short computation time. Finally, sliding contact was incorporated to mimic realistic boundary condition in error-sensitive regions. Mechanical simulation result was compared with commercial finite element software. And retrospective validation study with post-operative scan of four CMF cases was performed.

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Realistic modeling for facial animation

Cited by 661 publications

References 21 publications

Vision Based Speech Animation Transferring with Underlying Anatomical Structure

Vision Based Speech Animation Transferring with Underlying Anatomical Structure

Facial expression transfer method based on frequency analysis

A new soft-tissue simulation strategy for cranio-maxillofacial surgery using facial muscle template model

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