Generation of realistic human behaviour

As the use of computers and robots in our everyday lives increases so does the need for better interaction with these devices. Human-computer interaction relies on the ability to understand and generate human behavioural signals such as speech, facial expressions and motion. This thesis deals with the synthesis and evaluation of such signals, focusing not only on their intelligibility but also on their realism. Since these signals are often correlated, it is common for methods to drive the generation of one signal using another. The thesis begins by tackling the problem of speech-driven facial animation and proposing models capable of producing realistic animations from a single image and an audio clip. The goal of these models is to produce a video of a target person, whose lips move in accordance with the driving audio. Particular focus is also placed on a) generating spontaneous expression such as blinks, b) achieving audio-visual synchrony and c) transferring or producing natural head motion. The second problem addressed in this thesis is that of video-driven speech reconstruction, which aims at converting a silent video into waveforms containing speech. The method proposed for solving this problem is capable of generating intelligible and accurate speech for both seen and unseen speakers. The spoken content is correctly captured thanks to a perceptual loss, which uses features from pre-trained speech-driven animation models. The ability of the video-to-speech model to run in real-time allows its use in hearing assistive devices and telecommunications. The final work proposed in this thesis is a generic domain translation system, that can be used for any translation problem including those mapping across different modalities. The framework is made up of two networks performing translations in opposite directions and can be successfully applied to solve diverse sets of translation problems, including speech-driven animation and video-driven speech reconstruction.Open Acces

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 Photo-realistic Voice-bot

Technology is at the point where systems are capable of synthesizing video of human actors indistinguishably from ones in which the actor is present. This research investigates whether or not it is possible to use this technology in order to create a system which, allows video generation of a human actor, that is able to interact with a user through speech in real-time, whilst also remaining indistinguishable from a real human actor. In other words, a photo-realistic voicebot. The work discusses the motivations and ethics, but also presents and tests a prototype system. The prototype aims to take advantage of the latest in real-time video manipulation software to create a natural sounding conversation with an artificially synthesized video

TEXT-DRIVEN MOUTH ANIMATION FOR HUMAN COMPUTER INTERACTION WITH PERSONAL ASSISTANT

International audiencePersonal assistants are becoming more pervasive in our environments but still do not provide natural interactions. Their lack of realism in term of expressiveness and their lack of visual feedback can create frustrating experiences and make users lose patience. In this sense, we propose an end-to-end trainable neural architecture for text-driven 3D mouth animations. Previous works showed such architectures provide better realism and could open the door for integrated affective Human Computer Interface (HCI). Our study shows that such visual feedback improves users' comfort for 78% of the candidates significantly while slightly improving their time perception

Video-driven speech reconstruction using generative adversarial networks

Speech is a means of communication which relies on both audio and visual information. The absence of one modality can often lead to confusion or misinterpretation of information. In this paper we present an end-to-end temporal model capable of directly synthesising audio from silent video, without needing to transform to-and-from intermediate features. Our proposed approach, based on GANs is capable of producing natural sounding, intelligible speech which is synchronised with the video. The performance of our model is evaluated on the GRID dataset for both speaker dependent and speaker independent scenarios. To the best of our knowledge this is the first method that maps video directly to raw audio and the first to produce intelligible speech when tested on previously unseen speakers. We evaluate the synthesised audio not only based on the sound quality but also on the accuracy of the spoken words