Facial expression space learning

Chuang, Erika; Deshpande, F.; Bregler, Christoph

doi:10.1109/pccga.2002.1167840

Cited by 58 publications

(42 citation statements)

References 20 publications

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“…We compared our hierarchical model with a holistic representation model built in the same fashion and with Naive Bayesian Classifier (NBC) [4]. As our approach did not incorporate any temporal methods we didn't compare it with dynamic models such as MHMM [4].…”

Section: Methodsmentioning

confidence: 99%

2D Statistical Models of Facial Expressions for Realistic 3D Avatar Animation

Zalewski

Gong²

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)

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Section: Methodsmentioning

confidence: 99%

2D Statistical Models of Facial Expressions for Realistic 3D Avatar Animation

Zalewski

Gong²

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)

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“…A common method for dimensionality reduction is Principle Component Analysis (PCA) [12], which has been used in human figure and face shape representations [3,13,14]. Multi-Dimensional Scaling (MDS) [15] is another approach to finding an embedding that preserves the pairwise distances of original data.…”

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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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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“…They found that five to seven hidden perceptrons are probably enough to represent the space of facial expressions. Chuang et al [15] showed that the space of facial expression could be modeled with a bilinear model. Two formulations of bilinear models, asymmetric and symmetric, were fit to facial expression data.…”

Section: Related Workmentioning

confidence: 99%

Automatic 3D Facial Expression Analysis in Videos

Chang

Vieira

Turk

et al. 2005

Lecture Notes in Computer Science

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Abstract. We introduce a novel framework for automatic 3D facial expression analysis in videos. Preliminary results demonstrate editing facial expression with facial expression recognition. We first build a 3D expression database to learn the expression space of a human face. The real-time 3D video data were captured by a camera/projector scanning system. From this database, we extract the geometry deformation independent of pose and illumination changes. All possible facial deformations of an individual make a nonlinear manifold embedded in a high dimensional space. To combine the manifolds of different subjects that vary significantly and are usually hard to align, we transfer the facial deformations in all training videos to one standard model. Lipschitz embedding embeds the normalized deformation of the standard model in a low dimensional generalized manifold. We learn a probabilistic expression model on the generalized manifold. To edit a facial expression of a new subject in 3D videos, the system searches over this generalized manifold for optimal replacement with the 'target' expression, which will be blended with the deformation in the previous frames to synthesize images of the new expression with the current head pose. Experimental results show that our method works effectively.

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Facial expression space learning

Cited by 58 publications

References 20 publications

2D Statistical Models of Facial Expressions for Realistic 3D Avatar Animation

2D Statistical Models of Facial Expressions for Realistic 3D Avatar Animation

Vision Based Speech Animation Transferring with Underlying Anatomical Structure

Automatic 3D Facial Expression Analysis in Videos

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