A  large part of our communication is non-verbal:   humans use non-verbal behaviors to express various aspects of our state or intent.  Embodied artificial agents, such as virtual avatars or robots, should also use non-verbal behavior for efficient and pleasant interaction. A core part of non-verbal communication is gesticulation:  gestures communicate a large share of non-verbal content. For example, around 90\% of spoken utterances in descriptive discourse are accompanied by gestures. Since gestures are important, generating co-speech gestures has been an essential task in the Human-Agent Interaction (HAI) and Computer Graphics communities for several decades.  Evaluating the gesture-generating methods has been an equally important and equally challenging part of field development. Consequently, this thesis contributes to both the development and evaluation of gesture-generation models. 

This thesis proposes three deep-learning-based gesture-generation models. The first model is deterministic and uses only audio and generates only beat gestures.  The second model is deterministic and uses both audio and text, aiming to generate meaningful gestures.  A final model uses both audio and text and is probabilistic to learn the stochastic character of human gesticulation.  The methods have applications to both virtual agents and social robots. Individual research efforts in the field of gesture generation are difficult to compare, as there are no established benchmarks.  To address this situation, my colleagues and I launched the first-ever gesture-generation challenge, which we called the GENEA Challenge.  We have also investigated if online participants are as attentive as offline participants and found that they are both equally attentive provided that they are well paid.   Finally,  we developed a  system that integrates co-speech gesture-generation models into a real-time interactive embodied conversational agent.  This system is intended to facilitate the evaluation of modern gesture generation models in interaction. 

To further advance the development of capable gesture-generation methods, we need to advance their evaluation, and the research in the thesis supports an interpretation that evaluation is the main bottleneck that limits the field.  There are currently no comprehensive co-speech gesture datasets, which should be large, high-quality, and diverse. In addition, no strong objective metrics are yet available.  Creating speech-gesture datasets and developing objective metrics are highlighted as essential next steps for further field development.