Anna Deichler

Gestures play a crucial role in human communication, enhancing interpersonal interactions through non-verbal expression. Burgeoning technology allows virtual avatars to leverage communicative gestures to enhance their life-likeness and communication quality with AI-generated gestures. Traditionally, evaluations of AI-generated gestures have been confined to 2D settings. However, Virtual Reality (VR) offers an immersive alternative with the potential to affect the perception of virtual gestures. This paper introduces a novel evaluation approach for computer-generated gestures, investigating the impact of a fully immersive environment compared to a traditional 2D setting. The goal is to find the differences, benefits, and drawbacks of the two alternatives. Furthermore, the study also aims to investigate three gesture generation algorithms submitted to the 2023 GENEA Challenge and evaluate their performance in the two virtual settings. Experiments showed that the VR setting has an impact on the rating of generated gestures. Participants tended to rate gestures observed in VR slightly higher on average than in 2D. Furthermore, the results of the study showed that the generation models used for the study had a consistent ranking. However, the setting had a limited impact on the models’ performance, having a bigger impact on the perception of ’true movement’ which had higher ratings in VR than in 2D.

In this paper, we present a novel dataset captured using a VR headset to record conversations between participants within a physics simulator (AI2-THOR). Our primary objective is to extend the field of co-speech gesture generation by incorporating rich contextual information within referential settings. Participants engaged in various conversational scenarios, all based on referential communication tasks. The dataset provides a rich set of multimodal recordings such as motion capture, speech, gaze, and scene graphs. This comprehensive dataset aims to enhance the understanding and development of gesture generation models in 3D scenes by providing diverse and contextually rich data.

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.

This paper describes a system developed for the GENEA (Generation and Evaluation of Non-verbal Behaviour for Embodied Agents) Challenge 2023. Our solution builds on an existing diffusion-based motion synthesis model. We propose a contrastive speech and motion pretraining (CSMP) module, which learns a joint embedding for speech and gesture with the aim to learn a semantic coupling between these modalities. The output of the CSMP module is used as a conditioning signal in the diffusion-based gesture synthesis model in order to achieve semantically-aware co-speech gesture generation. Our entry achieved highest human-likeness and highest speech appropriateness rating among the submitted entries. This indicates that our system is a promising approach to achieve human-like co-speech gestures in agents that carry semantic meaning.

Emotional expressivity is essential for successful Human-Robot Interaction. However, robots often have different levels of expressivity in their face and voice. Here we ask whether this modality mismatch influences human behaviour and perception of the robot. Participants played a cooperative task with a robot that displayed matched and mismatched smiling expressions in the face and voice. Emotional expressivity did not influence acceptance of robot’s recommendations or subjective evaluations of the robot. However, we found that the robot had overall a higher social influence than a virtual character, and was evaluated more positively.