Jonas Beskow

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 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.

Engaging embodied conversational agents need to generate expressive behavior in order to be believable in socializing interactions. We present a system that can generate spontaneous speech with supporting lip movements. The neural conversational TTS voice is trained on a multi-style speech corpus that has been prosodically tagged (pitch and speaking rate) and transcribed (including tokens for breathing, fillers and laughter). We introduce a speech animation algorithm where articulatory effort can be adjusted. The facial animation is driven by time-stamped phonemes and prominence estimates from the synthesised speech waveform to modulate the lip-and jaw movements accordingly. In objective evaluations we show that the system is able to generate speech and facial animation that vary in articulation effort. In subjective evaluations we compare our conversational TTS system's capability to deliver jokes with a commercial TTS. Both system succeeded equally good.

With read-aloud speech synthesis achieving high naturalness scores, there is a growing research interest in synthesising spontaneous speech. However, human spontaneous face-to-face conversation has both spoken and non-verbal aspects (here, co-speech gestures). Only recently has research begun to explore the benefits of jointly synthesising these two modalities in a single system. The previous state of the art used non-probabilistic methods, which fail to capture the variability of human speech and motion, and risk producing oversmoothing artefacts and sub-optimal synthesis quality. We present the first diffusion-based probabilistic model, called Diff-TTSG, that jointly learns to synthesise speech and gestures together. Our method can be trained on small datasets from scratch. Furthermore, we describe a set of careful uni- and multi-modal subjective tests for evaluating integrated speech and gesture synthesis systems, and use them to validate our proposed approach. Please see https://shivammehta25.github.io/Diff-TTSG/ for video examples, data, and code.

Diffusion models have experienced a surge of interest as highly expressive yet efficiently trainable probabilistic models. We show that these models are an excellent fit for synthesising human motion that co-occurs with audio, e.g., dancing and co-speech gesticulation, since motion is complex and highly ambiguous given audio, calling for a probabilistic description. Specifically, we adapt the DiffWave architecture to model 3D pose sequences, putting Conformers in place of dilated convolutions for improved modelling power. We also demonstrate control over motion style, using classifier-free guidance to adjust the strength of the stylistic expression. Experiments on gesture and dance generation confirm that the proposed method achieves top-of-the-line motion quality, with distinctive styles whose expression can be made more or less pronounced. We also synthesise path-driven locomotion using the same model architecture. Finally, we generalise the guidance procedure to obtain product-of-expert ensembles of diffusion models and demonstrate how these may be used for, e.g., style interpolation, a contribution we believe is of independent interest.