Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Video Rewrite: driving visual speech with audio
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Trainable videorealistic speech animation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Sample-Based Synthesis of Photo-Realistic Talking Heads
CA '98 Proceedings of the Computer Animation
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Spacetime faces: high resolution capture for modeling and animation
ACM SIGGRAPH 2004 Papers
Expressive speech-driven facial animation
ACM Transactions on Graphics (TOG)
Expressive Facial Animation Synthesis by Learning Speech Coarticulation and Expression Spaces
IEEE Transactions on Visualization and Computer Graphics
eFASE: expressive facial animation synthesis and editing with phoneme-isomap controls
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Video-rate capture of dynamic face shape and appearance
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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We describe a method for the synthesis of visual speech movements using a hybrid unit selection/model-based approach. Speech lip movements are captured using a 3D stereo face capture system and split up into phonetic units. A dynamic parameterisation of this data is constructed which maintains the relationship between lip shapes and velocities; within this parameterisation a model of how lips move is built and is used in the animation of visual speech movements from speech audio input. The mapping from audio parameters to lip movements is disambiguated by selecting only the most similar stored phonetic units to the target utterance during synthesis. By combining properties of model-based synthesis (e.g., HMMs, neural nets) with unit selection we improve the quality of our speech synthesis.