Audio-to-Visual Speech Conversion using Deep Neural Networks

We study the problem of mapping from acoustic to visual speech with the goal of generating accurate, perceptually natural speech animation automatically from an audio speech signal. We present a sliding window deep neural network that learns a mapping from a window of acoustic features to a window of visual features from a large audio-visual speech dataset. Overlapping visual predictions are averaged to generate continuous, smoothly varying speech animation. We outperform a baseline HMM inversion approach in both objective and subjective evaluations and perform a thorough analysis of our results

Compressing Video Calls using Synthetic Talking Heads

We leverage the modern advancements in talking head generation to propose an end-to-end system for talking head video compression. Our algorithm transmits pivot frames intermittently while the rest of the talking head video is generated by animating them. We use a state-of-the-art face reenactment network to detect key points in the non-pivot frames and transmit them to the receiver. A dense flow is then calculated to warp a pivot frame to reconstruct the non-pivot ones. Transmitting key points instead of full frames leads to significant compression. We propose a novel algorithm to adaptively select the best-suited pivot frames at regular intervals to provide a smooth experience. We also propose a frame-interpolater at the receiver's end to improve the compression levels further. Finally, a face enhancement network improves reconstruction quality, significantly improving several aspects like the sharpness of the generations. We evaluate our method both qualitatively and quantitatively on benchmark datasets and compare it with multiple compression techniques. We release a demo video and additional information at https://cvit.iiit.ac.in/research/projects/cvit-projects/talking-video-compression.Comment: British Machine Vision Conference (BMVC), 202

Bi & tri dimensional scene description and composition in the MPEG-4 standard

MPEG-4 is a new ISO/IEC standard being developed by MPEG (Moving Picture Experts Group). The standard is to be released in November 1998 and version 1 will be an International Standard in January 1999 The MPEG-4 standard addresses the new demands that arise in a world in which more and more audio-visual material is exchanged in digital form MPEG-4 addresses the coding of objects of various types. Not only traditional video and audio frames, but also natural video and audio objects as well as textures, text, 2- and 3-dimensional graphic primitives, and synthetic music and sound effects. Using MPEG-4 to reconstruct an audio-visual scene at a terminal, it is hence no longer sufficient to encode the raw audio-visual data and transmit it, as MPEG-2 does m order to synchronize video and audio. In MPEG-4, all objects are multiplexed together at the encoder and transported to the terminal Once de-multiplexed, these objects are composed at the terminal to construct and present to the end user a meaningful audio-visual scene. The placement of these elementary audio-visual objects in space and time is described in the scene description of a scene. While the action of putting these objects together in the same representation space is the composition of audio-visual objects. My research was concerned with the scene description and composition of the audio-visual objects that are defined in an audio-visual scene Scene descriptions are coded independently irom sticams related to primitive audio-visual objects. The set of parameters belonging to the scene description are differentiated from the parameters that are used to improve the coding efficiency of an object. While the independent coding of different objects may achieve a higher compression rate, it also brings the ability to manipulate content at the terminal. This allows the modification of the scene description parameters without having to decode the primitive audio-visual objects themselves. This approach allows the development of a syntax that describes the spatio-temporal relationships of audio-visual scene objects. The behaviours of objects and their response to user inputs can thus also be represented in the scene description, allowing richer audio-visual content to be delivered as an MPEG-4 stream

Video coding for compression and content-based functionality

The lifetime of this research project has seen two dramatic developments in the area of digital video coding. The first has been the progress of compression research leading to a factor of two improvement over existing standards, much wider deployment possibilities and the development of the new international ITU-T Recommendation H.263. The second has been a radical change in the approach to video content production with the introduction of the content-based coding concept and the addition of scene composition information to the encoded bit-stream. Content-based coding is central to the latest international standards efforts from the ISO/IEC MPEG working group. This thesis reports on extensions to existing compression techniques exploiting a priori knowledge about scene content. Existing, standardised, block-based compression coding techniques were extended with work on arithmetic entropy coding and intra-block prediction. These both form part of the H.263 and MPEG-4 specifications respectively. Object-based coding techniques were developed within a collaborative simulation model, known as SIMOC, then extended with ideas on grid motion vector modelling and vector accuracy confidence estimation. An improved confidence measure for encouraging motion smoothness is proposed. Object-based coding ideas, with those from other model and layer-based coding approaches, influenced the development of content-based coding within MPEG-4. This standard made considerable progress in this newly adopted content based video coding field defining normative techniques for arbitrary shape and texture coding. The means to generate this information, the analysis problem, for the content to be coded was intentionally not specified. Further research work in this area concentrated on video segmentation and analysis techniques to exploit the benefits of content based coding for generic frame based video. The work reported here introduces the use of a clustering algorithm on raw data features for providing initial segmentation of video data and subsequent tracking of those image regions through video sequences. Collaborative video analysis frameworks from COST 21 l qual and MPEG-4, combining results from many other segmentation schemes, are also introduced

Animation of a hierarchical image based facial model and perceptual analysis of visual speech

In this Thesis a hierarchical image-based 2D talking head model is presented, together with robust automatic and semi-automatic animation techniques, and a novel perceptual method for evaluating visual-speech based on the McGurk effect. The novelty of the hierarchical facial model stems from the fact that sub-facial areas are modelled individually. To produce a facial animation, animations for a set of chosen facial areas are first produced, either by key-framing sub-facial parameter values, or using a continuous input speech signal, and then combined into a full facial output. Modelling hierarchically has several attractive qualities. It isolates variation in sub-facial regions from the rest of the face, and therefore provides a high degree of control over different facial parts along with meaningful image based animation parameters. The automatic synthesis of animations may be achieved using speech not originally included in the training set. The model is also able to automatically animate pauses, hesitations and non-verbal (or non-speech related) sounds and actions. To automatically produce visual-speech, two novel analysis and synthesis methods are proposed. The first method utilises a Speech-Appearance Model (SAM), and the second uses a Hidden Markov Coarticulation Model (HMCM) - based on a Hidden Markov Model (HMM). To evaluate synthesised animations (irrespective of whether they are rendered semi automatically, or using speech), a new perceptual analysis approach based on the McGurk effect is proposed. This measure provides both an unbiased and quantitative method for evaluating talking head visual speech quality and overall perceptual realism. A combination of this new approach, along with other objective and perceptual evaluation techniques, are employed for a thorough evaluation of hierarchical model animations.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Investigating 3D Visual Speech Animation Using 2D Videos

Lip motion accuracy is of paramount importance for speech intelligibility, especially for users who are hard of hearing or foreign language learners. Furthermore, generating a high level of realism in lip movements is required for the game and film production industries. This thesis focuses on the mapping of tracked lip motions of front-view 2D videos of a real speaker to a synthetic 3D head. A data-driven approach is used based on a 3D morphable model (3DMM) built using 3D synthetic head poses. The 3DMMs have been widely used for different tasks such as face recognition, detect facial expressions and lip motions in 2D videos. However, investigating factors such as the required facial landmarks for the mapping process, the amount of data for constructing the 3DMM, and differences in facial features between real faces and 3D faces that may influence the resulting animation have not been considered yet. Therefore, this research centers around investigating the impact of these factors on the final 3D lip motions. The thesis explores how different sets of facial features used in the mapping process influence the resulting 3D motions. Five sets of the facial features are used for mapping the real faces to the corresponding 3D faces. The results show that the inclusion of eyebrows, eyes, nose, and lips improves the 3D lip motions, while face contour features (i.e. the outside boundary of the front view of the face) restrict the face’s mesh, distorting the resulting animation. This thesis investigates how using different amounts of data when constructing the 3DMM affects the 3D lip motions. The results show that using a wider range of synthetic head poses for different phoneme intensities to create a 3DMM, as well as a combination of front- and side-view photographs of real speakers to produce initial neutral 3D synthetic head poses, provides better animation results compared to ground truth data consisting of front- and side-view 2D videos of real speakers. The thesis also investigates the impact of differences and similarities in facial features between real speakers and the 3DMMs on the resulting 3D lip motions by mapping between non-similar faces based on differences and similarities in vertical mouth height and mouth width. The objective and user test results show that mapping 2D videos of real speakers with low vertical mouth heights to 3D heads that correspond to real speakers with high vertical mouth heights, or vice versa, generates less good 3D lip motions. It is thus important that this is considered when using a 2D recording of a real actor’s lip movements to control a 3D synthetic character

Animation of a hierarchical image based facial model and perceptual analysis of visual speech

In this Thesis a hierarchical image-based 2D talking head model is presented, together with robust automatic and semi-automatic animation techniques, and a novel perceptual method for evaluating visual-speech based on the McGurk effect. The novelty of the hierarchical facial model stems from the fact that sub-facial areas are modelled individually. To produce a facial animation, animations for a set of chosen facial areas are first produced, either by key-framing sub-facial parameter values, or using a continuous input speech signal, and then combined into a full facial output. Modelling hierarchically has several attractive qualities. It isolates variation in sub-facial regions from the rest of the face, and therefore provides a high degree of control over different facial parts along with meaningful image based animation parameters. The automatic synthesis of animations may be achieved using speech not originally included in the training set. The model is also able to automatically animate pauses, hesitations and non-verbal (or non-speech related) sounds and actions. To automatically produce visual-speech, two novel analysis and synthesis methods are proposed. The first method utilises a Speech-Appearance Model (SAM), and the second uses a Hidden Markov Coarticulation Model (HMCM) - based on a Hidden Markov Model (HMM). To evaluate synthesised animations (irrespective of whether they are rendered semi automatically, or using speech), a new perceptual analysis approach based on the McGurk effect is proposed. This measure provides both an unbiased and quantitative method for evaluating talking head visual speech quality and overall perceptual realism. A combination of this new approach, along with other objective and perceptual evaluation techniques, are employed for a thorough evaluation of hierarchical model animations