Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers

We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality

Speech Enabled Avatar from a Single Photograph

This paper presents a complete framework for creating speech-enabled 2D and 3D avatars from a single image of a person. Our approach uses a generic facial motion model which represents deformations of the prototype face during speech. We have developed an HMM-based facial animation algorithm which takes into account both lexical stress and coarticulation. This algorithm produces realistic animations of the prototype facial surface from either text or speech. The generic facial motion model is transformed to a novel face geometry using a set of corresponding points between the generic mesh and the novel face. In the case of a 2D avatar, a single photograph of the person is used as input. We manually select a small number of features on the photograph and these are used to deform the prototype surface. The deformed surface is then used to animate the photograph. In the case of a 3D avatar, we use a single stereo image of the person as input. The sparse geometry of the face is computed from this image and used to warp the prototype surface to obtain the complete 3D surface of the person's face. This surface is etched into a glass cube using sub-surface laser engraving (SSLE) technology. Synthesized facial animation videos are then projected onto the etched glass cube. Even though the etched surface is static, the projection of facial animation onto it results in a compelling experience for the viewer. We show several examples of 2D and 3D avatars that are driven by text and speech inputs

Model-based synthesis of visual speech movements from 3D video

In this paper 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

Modelling talking human faces

This thesis investigates a number of new approaches for visual speech synthesis using data-driven methods to implement a talking face. The main contributions in this thesis are the following. The accuracy of shared Gaussian process latent variable model (SGPLVM) built using the active appearance model (AAM) and relative spectral transform-perceptual linear prediction (RASTAPLP) features is improved by employing a more accurate AAM. This is the first study to report that using a more accurate AAM improves the accuracy of SGPLVM. Objective evaluation via reconstruction error is performed to compare the proposed approach against previously existing methods. In addition, it is shown experimentally that the accuracy of AAM can be improved by using a larger number of landmarks and/or larger number of samples in the training data. The second research contribution is a new method for visual speech synthesis utilising a fully Bayesian method namely the manifold relevance determination (MRD) for modelling dynamical systems through probabilistic non-linear dimensionality reduction. This is the first time MRD was used in the context of generating talking faces from the input speech signal. The expressive power of this model is in the ability to consider non-linear mappings between audio and visual features within a Bayesian approach. An efficient latent space has been learnt iii Abstract iv using a fully Bayesian latent representation relying on conditional nonlinear independence framework. In the SGPLVM the structure of the latent space cannot be automatically estimated because of using a maximum likelihood formulation. In contrast to SGPLVM the Bayesian approaches allow the automatic determination of the dimensionality of the latent spaces. The proposed method compares favourably against several other state-of-the-art methods for visual speech generation, which is shown in quantitative and qualitative evaluation on two different datasets. Finally, the possibility of incremental learning of AAM for inclusion in the proposed MRD approach for visual speech generation is investigated. The quantitative results demonstrate that using MRD in conjunction with incremental AAMs produces only slightly less accurate results than using batch methods. These results support a way of training this kind of models on computers with limited resources, for example in mobile computing. Overall, this thesis proposes several improvements to the current state-of-the-art in generating talking faces from speech signal leading to perceptually more convincing results

Text-based Editing of Talking-head Video

Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis

A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation

Body language (BL) refers to the non-verbal communication expressed through physical movements, gestures, facial expressions, and postures. It is a form of communication that conveys information, emotions, attitudes, and intentions without the use of spoken or written words. It plays a crucial role in interpersonal interactions and can complement or even override verbal communication. Deep multi-modal learning techniques have shown promise in understanding and analyzing these diverse aspects of BL. The survey emphasizes their applications to BL generation and recognition. Several common BLs are considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and Talking Head (TH), and we have conducted an analysis and established the connections among these four BL for the first time. Their generation and recognition often involve multi-modal approaches. Benchmark datasets for BL research are well collected and organized, along with the evaluation of SOTA methods on these datasets. The survey highlights challenges such as limited labeled data, multi-modal learning, and the need for domain adaptation to generalize models to unseen speakers or languages. Future research directions are presented, including exploring self-supervised learning techniques, integrating contextual information from other modalities, and exploiting large-scale pre-trained multi-modal models. In summary, this survey paper provides a comprehensive understanding of deep multi-modal learning for various BL generations and recognitions for the first time. By analyzing advancements, challenges, and future directions, it serves as a valuable resource for researchers and practitioners in advancing this field. n addition, we maintain a continuously updated paper list for deep multi-modal learning for BL recognition and generation: https://github.com/wentaoL86/awesome-body-language

Audiovisual integration of emotional signals from others' social interactions

Audiovisual perception of emotions has been typically examined using displays of a solitary character (e.g., the face-voice and/or body-sound of one actor). However, in real life humans often face more complex multisensory social situations, involving more than one person. Here we ask if the audiovisual facilitation in emotion recognition previously found in simpler social situations extends to more complex and ecological situations. Stimuli consisting of the biological motion and voice of two interacting agents were used in two experiments. In Experiment 1, participants were presented with visual, auditory, auditory filtered/noisy, and audiovisual congruent and incongruent clips. We asked participants to judge whether the two agents were interacting happily or angrily. In Experiment 2, another group of participants repeated the same task, as in Experiment 1, while trying to ignore either the visual or the auditory information. The findings from both experiments indicate that when the reliability of the auditory cue was decreased participants weighted more the visual cue in their emotional judgments. This in turn translated in increased emotion recognition accuracy for the multisensory condition. Our findings thus point to a common mechanism of multisensory integration of emotional signals irrespective of social stimulus complexity