Movement is an essential aspect of our lives. Not only do we move to interact with our physical environment, but we also express ourselves and communicate with others through our movements. In an increasingly computerized world where various technologies and devices surround us, our movements are essential parts of our interaction with and consumption of computational devices and artifacts. In this context, incorporating an understanding of our movements within the design of the technologies surrounding us can significantly improve our daily experiences. This need has given rise to the field of movement computing - developing computational models of movement that can perceive, manipulate, and generate movements. In this thesis, we contribute to the field of movement computing by building machine-learning-based solutions for automatic movement generation. In particular, we focus on using machine learning techniques and motion capture data to create controllable, generative movement models. We also contribute to the field by creating datasets, tools, and libraries that we have developed during our research. We start our research by reviewing the works on building automatic movement generation systems using machine learning techniques and motion capture data. Our review covers background topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. Building on our literature review, we present WalkNet, an interactive agent walking movement controller based on neural networks. The expressivity of virtual, animated agents plays an essential role in their believability. Therefore, WalkNet integrates controlling the expressive qualities of movement with the goal-oriented behaviour of an animated virtual agent. It allows us to control the generation based on the valence and arousal levels of affect, the movement's walking direction, and the mover's movement signature in real-time. Following WalkNet, we look at controlling movement generation using more complex stimuli such as music represented by audio signals (i.e., non-symbolic music). Music-driven dance generation involves a highly non-linear mapping between temporally dense stimuli (i.e., the audio signal) and movements, which renders a more challenging modelling movement problem. To this end, we present GrooveNet, a real-time machine learning model for music-driven dance generation.

This book constitutes the proceedings of the 17th International Conference on Intelligent Virtual Agents, IVA 2017, held in Stockholm, Sweden, in August 2017. The 30 regular papers and 31 demo papers presented in this volume were carefully reviewed and selected from 78 submissions. The annual IVA conference represents the main interdisciplinary scientic forum for presenting research on modeling, developing, and evaluating intelligent virtual agents (IVAs) with a focus on communicative abilities and social behavior.

The construction of a natural interactive human-computer interaction has become an integral component of intelligent system development, constituting a core subject within the field of human-computer interaction. Grounded in the utilization of human perception theories, this study proposes a robust method for speech emotion recognition and a model for inferring user emotional state changes, thereby achieving a hu-man-computer interaction experience characterized by both listening and articulating, and conveying sentiments effectively. Simultaneously, focusing on the audio-visual modalities within human-computer interaction, this research explores methods for con-structing interactive feedback in auditory and visual modalities within real hu-man-computer dialog scenarios. This aims to synchronize textual, verbal, and visual expressions in human-computer interaction, enhancing its naturalness, improving user experience, and augmenting satisfaction and pleasure during interaction. The research contributions are as follows: Introducing a method that robustly identifies user emotional states in real hu-man-computer speech dialogue scenarios through the utilization of local and global attention mechanisms. This method generates robust and distinguishable representa-tions of speech emotion, thereby enhancing the robustness and accuracy of speech emotion recognition systems in authentic dialogue scenarios.

This book provides comprehensive, authoritative surveys covering the modeling, automatic detection, analysis, and synthesis of nonverbal social signals.

This book constitutes the refereed post-conference proceedings the 11th EAI International Conference on ArtsIT, Interactivity and Game Creation, ArtsIT 2022 which was held in Faro, Portugal, November 21-22, 2022. The 45 revised full papers presented were carefully selected from 118 submissions. The papers are thematically arranged in the following sections: Dialogues Between Geometry, Computer Graphics and the Visual Arts; Games and Gamification; Museums and the Virtual; Animation, AI, Books and Behavior; Fluency, Fashion, Emotion and Play; Movement, Film and Audio.