LivelySpeaker: Towards Semantic-Aware Co-Speech Gesture Generation

Gestures are non-verbal but important behaviors accompanying people's speech. While previous methods are able to generate speech rhythm-synchronized gestures, the semantic context of the speech is generally lacking in the gesticulations. Although semantic gestures do not occur very regularly in human speech, they are indeed the key for the audience to understand the speech context in a more immersive environment. Hence, we introduce LivelySpeaker, a framework that realizes semantics-aware co-speech gesture generation and offers several control handles. In particular, our method decouples the task into two stages: script-based gesture generation and audio-guided rhythm refinement. Specifically, the script-based gesture generation leverages the pre-trained CLIP text embeddings as the guidance for generating gestures that are highly semantically aligned with the script. Then, we devise a simple but effective diffusion-based gesture generation backbone simply using pure MLPs, that is conditioned on only audio signals and learns to gesticulate with realistic motions. We utilize such powerful prior to rhyme the script-guided gestures with the audio signals, notably in a zero-shot setting. Our novel two-stage generation framework also enables several applications, such as changing the gesticulation style, editing the co-speech gestures via textual prompting, and controlling the semantic awareness and rhythm alignment with guided diffusion. Extensive experiments demonstrate the advantages of the proposed framework over competing methods. In addition, our core diffusion-based generative model also achieves state-of-the-art performance on two benchmarks. The code and model will be released to facilitate future research.Comment: Accepted by ICCV 202

Expressing Robot Personality through Talking Body Language

Social robots must master the nuances of human communication as a mean to convey an effective message and generate trust. It is well-known that non-verbal cues are very important in human interactions, and therefore a social robot should produce a body language coherent with its discourse. In this work, we report on a system that endows a humanoid robot with the ability to adapt its body language according to the sentiment of its speech. A combination of talking beat gestures with emotional cues such as eye lightings, body posture of voice intonation and volume permits a rich variety of behaviors. The developed approach is not purely reactive, and it easily allows to assign a kind of personality to the robot. We present several videos with the robot in two different scenarios, and showing discrete and histrionic personalities.This work has been partially supported by the Basque Government (IT900-16 and Elkartek 2018/00114), the Spanish Ministry of Economy and Competitiveness (RTI 2018-093337-B-100, MINECO/FEDER, EU)

Towards a framework for socially interactive robots

250 p.En las últimas décadas, la investigación en el campo de la robótica social ha crecido considerablemente. El desarrollo de diferentes tipos de robots y sus roles dentro de la sociedad se están expandiendo poco a poco. Los robots dotados de habilidades sociales pretenden ser utilizados para diferentes aplicaciones; por ejemplo, como profesores interactivos y asistentes educativos, para apoyar el manejo de la diabetes en niños, para ayudar a personas mayores con necesidades especiales, como actores interactivos en el teatro o incluso como asistentes en hoteles y centros comerciales.El equipo de investigación RSAIT ha estado trabajando en varias áreas de la robótica, en particular,en arquitecturas de control, exploración y navegación de robots, aprendizaje automático y visión por computador. El trabajo presentado en este trabajo de investigación tiene como objetivo añadir una nueva capa al desarrollo anterior, la capa de interacción humano-robot que se centra en las capacidades sociales que un robot debe mostrar al interactuar con personas, como expresar y percibir emociones, mostrar un alto nivel de diálogo, aprender modelos de otros agentes, establecer y mantener relaciones sociales, usar medios naturales de comunicación (mirada, gestos, etc.),mostrar personalidad y carácter distintivos y aprender competencias sociales.En esta tesis doctoral, tratamos de aportar nuestro grano de arena a las preguntas básicas que surgen cuando pensamos en robots sociales: (1) ¿Cómo nos comunicamos (u operamos) los humanos con los robots sociales?; y (2) ¿Cómo actúan los robots sociales con nosotros? En esa línea, el trabajo se ha desarrollado en dos fases: en la primera, nos hemos centrado en explorar desde un punto de vista práctico varias formas que los humanos utilizan para comunicarse con los robots de una maneranatural. En la segunda además, hemos investigado cómo los robots sociales deben actuar con el usuario.Con respecto a la primera fase, hemos desarrollado tres interfaces de usuario naturales que pretenden hacer que la interacción con los robots sociales sea más natural. Para probar tales interfaces se han desarrollado dos aplicaciones de diferente uso: robots guía y un sistema de controlde robot humanoides con fines de entretenimiento. Trabajar en esas aplicaciones nos ha permitido dotar a nuestros robots con algunas habilidades básicas, como la navegación, la comunicación entre robots y el reconocimiento de voz y las capacidades de comprensión.Por otro lado, en la segunda fase nos hemos centrado en la identificación y el desarrollo de los módulos básicos de comportamiento que este tipo de robots necesitan para ser socialmente creíbles y confiables mientras actúan como agentes sociales. Se ha desarrollado una arquitectura(framework) para robots socialmente interactivos que permite a los robots expresar diferentes tipos de emociones y mostrar un lenguaje corporal natural similar al humano según la tarea a realizar y lascondiciones ambientales.La validación de los diferentes estados de desarrollo de nuestros robots sociales se ha realizado mediante representaciones públicas. La exposición de nuestros robots al público en esas actuaciones se ha convertido en una herramienta esencial para medir cualitativamente la aceptación social de los prototipos que estamos desarrollando. De la misma manera que los robots necesitan un cuerpo físico para interactuar con el entorno y convertirse en inteligentes, los robots sociales necesitan participar socialmente en tareas reales para las que han sido desarrollados, para así poder mejorar su sociabilida

An Evaluation Model For Speech-Driven Gesture Synthesis

The research and development of embodied agents with advanced relational capabilities is constantly evolving. In recent years, the development of behavioural signal generation models to be integrated in social robots and virtual characters, is moving from rule-based to data-driven approaches, requiring appropriate and reliable evaluation techniques. This work proposes a novel machine learning approach for the evaluation of speech-to-gestures models that is independent from the audio source. This approach enables the measurement of the quality of gestures produced by these models and provides a benchmark for their evaluation. Results show that the proposed approach is consistent with evaluations made through user studies and, furthermore, that its use allows for a reliable comparison of speech-to-gestures state-of-the-art models

A Comprehensive Review of Data-Driven Co-Speech Gesture Generation

Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.Comment: Accepted for EUROGRAPHICS 202

Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition

This paper describes a novel method called Deep Dynamic Neural Networks (DDNN) for multimodal gesture recognition. A semi-supervised hierarchical dynamic framework based on a Hidden Markov Model (HMM) is proposed for simultaneous gesture segmentation and recognition where skeleton joint information, depth and RGB images, are the multimodal input observations. Unlike most traditional approaches that rely on the construction of complex handcrafted features, our approach learns high-level spatiotemporal representations using deep neural networks suited to the input modality: a Gaussian-Bernouilli Deep Belief Network (DBN) to handle skeletal dynamics, and a 3D Convolutional Neural Network (3DCNN) to manage and fuse batches of depth and RGB images. This is achieved through the modeling and learning of the emission probabilities of the HMM required to infer the gesture sequence. This purely data driven approach achieves a Jaccard index score of 0.81 in the ChaLearn LAP gesture spotting challenge. The performance is on par with a variety of state-of-the-art hand-tuned feature-based approaches and other learning-based methods, therefore opening the door to the use of deep learning techniques in order to further explore multimodal time series data

Plays of proximity and distance: Gesture-based interaction and visual music

This thesis presents the relations between gestural interfaces and artworks which deal with real- time and simultaneous performance of dynamic imagery and sound, the so called visual music practices. Those relation extend from a historical, practical and theoretical viewpoint, which this study aims to cover, at least partially, all of them. Such relations are exemplified by two artistic projects developed by the author of this thesis, which work as a starting point for analysing the issues around the two main topics. The principles, patterns, challenges and concepts which struc- tured the two artworks are extracted, analysed and discussed, providing elements for comparison and evaluation, which may be useful for future researches on the topic

Language as the Medium: A Literature Review. Harnessing the Prolific Power of Dramatic Language as a Therapeutic Tool in Drama Therapy

Language in and of the theatre, with its palate of variegated writing styles and playwrights from throughout time, has the potential to be harnessed, focused, and systematized for use as a therapeutic tool within drama therapy – the field’s artistic medium. Drama therapy could benefit from having a specific medium germane to its artform which has the potential to provide practitioners with a common resource and means of communication, assessment, diagnosis, and treatment planning, as well as align the field with other creative arts therapies. Language encompasses all forms of human communication – speaking, writing, signing, gesturing, expressing facially – and voice and speech training are crucial components of holistic training for actors. This thesis positions language as the primary medium of the theatre by examining multiple and disputed theories of theatre origins as well as interrelated theories of the theatre as an entity in order to distinguish drama as an evolutionary means of human communication. Furthermore, it probes the basic clinical foundations of speech-language pathology, which possesses a wealth of research and theory already supporting language assessment and treatment, in addition to basic biology and neuroanatomy surrounding speech, speech production, and development in order to ground the argument in pre-existing medical and clinical knowledge. Herein, I propose future directions and lay the groundwork for continued research and structured theory development