  This paper focuses on enhancing human-agent communication by integrating
spatial context into virtual agents' non-verbal behaviors, specifically
gestures. Recent advances in co-speech gesture generation have primarily
utilized data-driven methods, which create natural motion but limit the scope
of gestures to those performed in a void. Our work aims to extend these methods
by enabling generative models to incorporate scene information into
speech-driven gesture synthesis. We introduce a novel synthetic gesture dataset
tailored for this purpose. This development represents a critical step toward
creating embodied conversational agents that interact more naturally with their
environment and users.
