Matthias Odisio

Audiovisual Speech Synthesis

This paper presents the main approaches used to synthesize talking faces, and provides greater detail on a handful of these approaches. An attempt is made to distinguish between facial synthesis itself (i.e. the manner in which facial movements are rendered on a computer screen), and the way these movements may be controlled and predicted using phonetic input. The two main synthesis techniques (model-based vs. image-based) are contrasted and presented by a brief description of the most illustrative existing systems. The challenging issues—evaluation, data acquisition and modeling—that may drive future models are also discussed and illustrated by our current work at ICP.

Evaluation of movement generation systems using the point-light technique

We describe a comparative evaluation of different movement generation systems capable of computing articulatory trajectories from phonetic input. The articulatory trajectories here pilot the facial deformation of a 3D clone of a human female speaker. We test the adequacy of the predicted trajectories in accompanying the production of natural utterances. The performance of these predictions are compared to the ones of natural articulatory trajectories produced by the speaker and estimated by an original video-based motion capture technique. The test uses the point-light technique (Rosenblum, L.D. and Saldana, H.M., 1996; 1998).

Tracking talking faces with shape and appearance models

Abstract This paper presents a system that can recover and track the 3D speech movements of a speaker’s face for each image of a monocular sequence. To handle both the individual specificities of the speaker’s articulation and the complexity of the facial deformations during speech, speaker-specific articulated models of the face geometry and appearance are first built from real data. These face models are used for tracking: articulatory parameters are extracted for each image by an analysis-by-synthesis loop. The geometric model is linearly controlled by only seven articulatory parameters. Appearance is seen either as a classical texture map or through local appearance of a relevant subset of 3D points. We compare several appearance models: they are either constant or depend linearly on the articulatory parameters. We compare tracking results using these different appearance models with ground truth data not only in terms of recovery errors of the 3D geometry but also in terms of intelligibility enhancement provided by the movements.

Shape and appearance models of talking faces for model-based tracking

We present a system that can recover and track the 3D speech movements of a speakers face for each image of a monocular sequence. A speaker-specific face model is used for tracking: model parameters are extracted from each image by an analysis-by-synthesis loop. To handle both the individual specificities of the speakers articulation and the complexity of the facial deformations during speech, speaker-specific models of the face 3D geometry and appearance are built from real data. The geometric model is linearly controlled by only six articulatory parameters. Appearance is seen either as a classical texture map or through local appearance of a relevant subset of 3D points. We compare several appearance models: they are either constant or depend linearly on the articulatory parameters. We evaluate these different appearance models with ground truth data.