Principal components of expressive speech animation

Kshirsagar, Sumedha; Molet, Tom; Magnenat‐Thalmann, Nadia

doi:10.1109/cgi.2001.934656

Cited by 43 publications

(29 citation statements)

References 9 publications

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“…The authors also acknowledge their lack of subjective evaluation. Kshirsagar et al [2001] attempt to produce expressive visual animation using PCA. They capture expressive visual speech from an actor using optical facial markers placed to imitate the MPEG-4 FAPs standard Ostermann [2002].…”

Section: Concatenative Approachesmentioning

confidence: 99%

Expressive Modulation of Neutral Visual Speech

Shaw

Theobald

2016

IEEE MultiMedia

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Section: Concatenative Approachesmentioning

confidence: 99%

Expressive Modulation of Neutral Visual Speech

Shaw

Theobald

2016

IEEE MultiMedia

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“…This approach has been widely used in computer vision and computer animation communities. 16,[27][28][29][30][31] Among the linear combination of a set of prototypes, viseme blending is a special example of the linear combination of a set of shapes. The viseme blending approach has been proven to be a very efficient way to synthesize visible speech in most 3D animation authoring tools such as 3Ds MAX and MAYA.…”

Section: Accurate Automatic Visible Speech Synthesismentioning

confidence: 99%

Accurate automatic visible speech synthesis of arbitrary 3D models based on concatenation of diviseme motion capture data

Cole²,

Pellom³

et al. 2004

Computer Animation & Virtual

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We present a technique for accurate automatic visible speech synthesis from textual input. When provided with a speech waveform and the text of a spoken sentence, the system produces accurate visible speech synchronized with the audio signal. To develop the system, we collected motion capture data from a speaker's face during production of a set of words containing all diviseme sequences in English. The motion capture points from the speaker's face are retargeted to the vertices of the polygons of a 3D face model. When synthesizing a new utterance, the system locates the required sequence of divisemes, shrinks or expands each diviseme based on the desired phoneme segment durations in the target utterance, then moves the polygons in the regions of the lips and lower face to correspond to the spatial coordinates of the motion capture data. The motion mapping is realized by a key-shape mapping function learned by a set of viseme examples in the source and target faces. A well-posed numerical algorithm estimates the shape blending coefficients. Time warping and motion vector blending at the juncture of two divisemes and the algorithm to search the optimal concatenated visible speech are also developed to provide the final concatenative motion sequence.

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“…We use MPEG-4 Facial Animation Parameters as low level facial deformation parameters [12]. However, for defining the visemes and expressions, we use the Principal Components (PCs) [13]. The PCs are derived from the statistical analysis of the facial motion data and reflect independent facial movements observed during fluent speech.…”

Section: Visual Front-endmentioning

confidence: 99%

A Model for Personality and Emotion Simulation

Egges

Kshirsagar

Magnenat‐Thalmann

2003

Lecture Notes in Computer Science

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Abstract. This paper describes a generic model for personality, mood and emotion simulation for conversational virtual humans. We present a generic model for describing and updating the parameters related to emotional behaviour. Also, this paper explores how existing theories for appraisal can be integrated into the framework. Finally we describe a prototype system that uses the described models in combination with a dialogue system and a talking head with synchronised speech and facial expressions.

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Principal components of expressive speech animation

Cited by 43 publications

References 9 publications

Expressive Modulation of Neutral Visual Speech

Expressive Modulation of Neutral Visual Speech

Accurate automatic visible speech synthesis of arbitrary 3D models based on concatenation of diviseme motion capture data

A Model for Personality and Emotion Simulation

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