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Mehta, S., Deichler, A., O'Regan, J., Moëll, B., Beskow, J., Henter, G. E. & Alexanderson, S. (2024). Fake it to make it: Using synthetic data to remedy the data shortage in joint multimodal speech-and-gesture synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition: . Paper presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1952-1964).
Open this publication in new window or tab >>Fake it to make it: Using synthetic data to remedy the data shortage in joint multimodal speech-and-gesture synthesis
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2024 (English)In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, p. 1952-1964Conference paper, Published paper (Refereed)
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-355103 (URN)
Conference
IEEE/CVF Conference on Computer Vision and Pattern Recognition
Projects
bodytalk
Note

QC 20241022

Available from: 2024-10-22 Created: 2024-10-22 Last updated: 2024-10-22Bibliographically approved
Deichler, A., Alexanderson, S. & Beskow, J. (2024). Incorporating Spatial Awareness in Data-Driven Gesture Generation for Virtual Agents. In: Proceedings of the 24th ACM International Conference on Intelligent Virtual Agents, IVA 2024: . Paper presented at 24th ACM International Conference on Intelligent Virtual Agents, IVA 2024, co-located with the Affective Computing and Intelligent Interaction 2024 Conference, ACII 2024, Glasgow, United Kingdom of Great Britain and Northern Ireland, September 16-19, 2024. Association for Computing Machinery (ACM), Article ID 42.
Open this publication in new window or tab >>Incorporating Spatial Awareness in Data-Driven Gesture Generation for Virtual Agents
2024 (English)In: Proceedings of the 24th ACM International Conference on Intelligent Virtual Agents, IVA 2024, Association for Computing Machinery (ACM) , 2024, article id 42Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2024
Keywords
Co-speech gesture, Deictic gestures, Gesture generation, Situated virtual agents, Synthetic data
National Category
Human Computer Interaction Computer Sciences
Identifiers
urn:nbn:se:kth:diva-359256 (URN)10.1145/3652988.3673936 (DOI)001441957400042 ()2-s2.0-85215524347 (Scopus ID)
Conference
24th ACM International Conference on Intelligent Virtual Agents, IVA 2024, co-located with the Affective Computing and Intelligent Interaction 2024 Conference, ACII 2024, Glasgow, United Kingdom of Great Britain and Northern Ireland, September 16-19, 2024
Note

Part of ISBN 9798400706257

QC 20250203

Available from: 2025-01-29 Created: 2025-01-29 Last updated: 2025-04-30Bibliographically approved
Mehta, S., Tu, R., Alexanderson, S., Beskow, J., Székely, É. & Henter, G. E. (2024). Unified speech and gesture synthesis using flow matching. In: 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2024): . Paper presented at 49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), APR 14-19, 2024, Seoul, SOUTH KOREA (pp. 8220-8224). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Unified speech and gesture synthesis using flow matching
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2024 (English)In: 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2024), Institute of Electrical and Electronics Engineers (IEEE) , 2024, p. 8220-8224Conference paper, Published paper (Refereed)
Abstract [en]

As text-to-speech technologies achieve remarkable naturalness in read-aloud tasks, there is growing interest in multimodal synthesis of verbal and non-verbal communicative behaviour, such as spontaneous speech and associated body gestures. This paper presents a novel, unified architecture for jointly synthesising speech acoustics and skeleton-based 3D gesture motion from text, trained using optimal-transport conditional flow matching (OT-CFM). The proposed architecture is simpler than the previous state of the art, has a smaller memory footprint, and can capture the joint distribution of speech and gestures, generating both modalities together in one single process. The new training regime, meanwhile, enables better synthesis quality in much fewer steps (network evaluations) than before. Uni- and multimodal subjective tests demonstrate improved speech naturalness, gesture human-likeness, and cross-modal appropriateness compared to existing benchmarks.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords
Text-to-speech, co-speech gestures, speech-to-gesture, integrated speech and gesture synthesis, ODE models
National Category
Comparative Language Studies and Linguistics
Identifiers
urn:nbn:se:kth:diva-361616 (URN)10.1109/ICASSP48485.2024.10445998 (DOI)001396233801103 ()2-s2.0-105001488767 (Scopus ID)
Conference
49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), APR 14-19, 2024, Seoul, SOUTH KOREA
Note

Part of ISBN 979-8-3503-4486-8,  979-8-3503-4485-1

QC 20250402

Available from: 2025-04-02 Created: 2025-04-02 Last updated: 2025-04-09Bibliographically approved
Gustafsson, J., Székely, É., Alexanderson, S. & Beskow, J. (2023). Casual chatter or speaking up? Adjusting articulatory effort in generation of speech and animation for conversational characters. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition, FG 2023: . Paper presented at 17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023, Waikoloa Beach, United States of America, Jan 5 2023 - Jan 8 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Casual chatter or speaking up? Adjusting articulatory effort in generation of speech and animation for conversational characters
2023 (English)In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition, FG 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Embodied conversational agents and social robots need to be able to generate spontaneous behavior in order to be believable in social interactions. We present a system that can generate spontaneous speech with supporting lip movements. The conversational TTS voice is trained on a podcast corpus that has been prosodically tagged (f0, speaking rate and energy) and transcribed (including tokens for breathing, fillers and laughter). We introduce a speech animation algorithm where articulatory effort can be adjusted. The speech animation is driven by time-stamped phonemes obtained from the internal alignment attention map of the TTS system, and we use prominence estimates from the synthesised speech waveform to modulate the lip- and jaw movements accordingly.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Natural Language Processing
Identifiers
urn:nbn:se:kth:diva-350326 (URN)10.1109/FG57933.2023.10042520 (DOI)2-s2.0-85149343136 (Scopus ID)
Conference
17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023, Waikoloa Beach, United States of America, Jan 5 2023 - Jan 8 2023
Note

Part of ISBN 9798350345445

QC 20240711

Available from: 2024-07-11 Created: 2024-07-11 Last updated: 2025-02-07Bibliographically approved
Deichler, A., Mehta, S., Alexanderson, S. & Beskow, J. (2023). Difusion-Based Co-Speech Gesture Generation Using Joint Text and Audio Representation. In: PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, ICMI 2023: . Paper presented at 25th International Conference on Multimodal Interaction (ICMI), OCT 09-13, 2023, Sorbonne Univ, Paris, FRANCE (pp. 755-762). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Difusion-Based Co-Speech Gesture Generation Using Joint Text and Audio Representation
2023 (English)In: PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, ICMI 2023, Association for Computing Machinery (ACM) , 2023, p. 755-762Conference paper, Published paper (Refereed)
Abstract [en]

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 difusion-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 difusion-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.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
Keywords
gesture generation, motion synthesis, difusion models, contrastive pre-training, semantic gestures
National Category
Natural Language Processing
Identifiers
urn:nbn:se:kth:diva-343773 (URN)10.1145/3577190.3616117 (DOI)001147764700093 ()2-s2.0-85170496681 (Scopus ID)
Conference
25th International Conference on Multimodal Interaction (ICMI), OCT 09-13, 2023, Sorbonne Univ, Paris, FRANCE
Note

Part of proceedings ISBN 979-8-4007-0055-2

QC 20240222

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2025-02-07Bibliographically approved
Deichler, A., Wang, S., Alexanderson, S. & Beskow, J. (2023). Learning to generate pointing gestures in situated embodied conversational agents. Frontiers in Robotics and AI, 10, Article ID 1110534.
Open this publication in new window or tab >>Learning to generate pointing gestures in situated embodied conversational agents
2023 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 10, article id 1110534Article in journal (Refereed) Published
Abstract [en]

One of the main goals of robotics and intelligent agent research is to enable them to communicate with humans in physically situated settings. Human communication consists of both verbal and non-verbal modes. Recent studies in enabling communication for intelligent agents have focused on verbal modes, i.e., language and speech. However, in a situated setting the non-verbal mode is crucial for an agent to adapt flexible communication strategies. In this work, we focus on learning to generate non-verbal communicative expressions in situated embodied interactive agents. Specifically, we show that an agent can learn pointing gestures in a physically simulated environment through a combination of imitation and reinforcement learning that achieves high motion naturalness and high referential accuracy. We compared our proposed system against several baselines in both subjective and objective evaluations. The subjective evaluation is done in a virtual reality setting where an embodied referential game is played between the user and the agent in a shared 3D space, a setup that fully assesses the communicative capabilities of the generated gestures. The evaluations show that our model achieves a higher level of referential accuracy and motion naturalness compared to a state-of-the-art supervised learning motion synthesis model, showing the promise of our proposed system that combines imitation and reinforcement learning for generating communicative gestures. Additionally, our system is robust in a physically-simulated environment thus has the potential of being applied to robots.

Place, publisher, year, edition, pages
Frontiers Media SA, 2023
Keywords
reinforcement learning, imitation learning, non-verbal communication, embodied interactive agents, gesture generation, physics-aware machine learning
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:kth:diva-326625 (URN)10.3389/frobt.2023.1110534 (DOI)000970385800001 ()37064574 (PubMedID)2-s2.0-85153351800 (Scopus ID)
Note

QC 20230508

Available from: 2023-05-08 Created: 2023-05-08 Last updated: 2023-05-08Bibliographically approved
Alexanderson, S., Nagy, R., Beskow, J. & Henter, G. E. (2023). Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models. ACM Transactions on Graphics, 42(4), Article ID 44.
Open this publication in new window or tab >>Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models
2023 (English)In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 42, no 4, article id 44Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
Keywords
conformers, dance, diffusion models, ensemble models, generative models, gestures, guided interpolation, locomotion, machine learning, product of experts
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-335345 (URN)10.1145/3592458 (DOI)001044671300010 ()2-s2.0-85166332883 (Scopus ID)
Note

QC 20230907

Available from: 2023-09-07 Created: 2023-09-07 Last updated: 2023-09-22Bibliographically approved
Deichler, A., Wang, S., Alexanderson, S. & Beskow, J. (2022). Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation. In: : . Paper presented at Context-Awareness in Human-Robot Interaction: Approaches and Challenges, workshop at 2022 ACM/IEEE International Conference on Human-Robot Interaction (pp. 2022).
Open this publication in new window or tab >>Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation
2022 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Pointing is an important mode of interaction with robots. While large amounts of prior studies focus on recognition of human pointing, there is a lack of investigation into generating context-aware human-like pointing gestures, a shortcoming we hope to address. We first collect a rich dataset of human pointing gestures and corresponding pointing target locations with accurate motion capture. Analysis of the dataset shows that it contains various pointing styles, handedness, and well-distributed target positions in surrounding 3D space in both single-target pointing scenario and two-target point-and-place.We then train reinforcement learning (RL) control policies in physically realistic simulation to imitate the pointing motion in the dataset while maximizing pointing precision reward.We show that our RL motion imitation setup allows models to learn human-like pointing dynamics while maximizing task reward (pointing precision). This is promising for incorporating additional context in the form of task reward to enable flexible context-aware pointing behaviors in a physically realistic environment while retaining human-likeness in pointing motion dynamics.

Keywords
motion generation, reinforcement learning, referring actions, pointing gestures, human-robot interaction, motion capture
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-313480 (URN)
Conference
Context-Awareness in Human-Robot Interaction: Approaches and Challenges, workshop at 2022 ACM/IEEE International Conference on Human-Robot Interaction
Note

QC 20220607

Available from: 2022-06-03 Created: 2022-06-03 Last updated: 2022-06-25Bibliographically approved
Wang, S., Alexanderson, S., Gustafsson, J., Beskow, J., Henter, G. E. & Székely, É. (2021). Integrated Speech and Gesture Synthesis. In: ICMI 2021 - Proceedings of the 2021 International Conference on Multimodal Interaction: . Paper presented at ICMI '21: International Conference on Multimodal Interaction, Montréal, QC, Canada, October 18-22, 2021 (pp. 177-185). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Integrated Speech and Gesture Synthesis
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2021 (English)In: ICMI 2021 - Proceedings of the 2021 International Conference on Multimodal Interaction, Association for Computing Machinery (ACM) , 2021, p. 177-185Conference paper, Published paper (Refereed)
Abstract [en]

Text-to-speech and co-speech gesture synthesis have until now been treated as separate areas by two different research communities, and applications merely stack the two technologies using a simple system-level pipeline. This can lead to modeling inefficiencies and may introduce inconsistencies that limit the achievable naturalness. We propose to instead synthesize the two modalities in a single model, a new problem we call integrated speech and gesture synthesis (ISG). We also propose a set of models modified from state-of-the-art neural speech-synthesis engines to achieve this goal. We evaluate the models in three carefully-designed user studies, two of which evaluate the synthesized speech and gesture in isolation, plus a combined study that evaluates the models like they will be used in real-world applications - speech and gesture presented together. The results show that participants rate one of the proposed integrated synthesis models as being as good as the state-of-the-art pipeline system we compare against, in all three tests. The model is able to achieve this with faster synthesis time and greatly reduced parameter count compared to the pipeline system, illustrating some of the potential benefits of treating speech and gesture synthesis together as a single, unified problem.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2021
Keywords
gesture generation, neural networks, speech synthesis, Piping systems, Neural-networks, Pipeline systems, Research applications, Research communities, Simple system, Single models, State of the art, System level pipelines, Text to speech, Water pipelines
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-313183 (URN)10.1145/3462244.3479914 (DOI)2-s2.0-85118992736 (Scopus ID)
Conference
ICMI '21: International Conference on Multimodal Interaction, Montréal, QC, Canada, October 18-22, 2021
Note

Part of proceedings ISBN 9781450384810

QC 20220602

Available from: 2022-06-02 Created: 2022-06-02 Last updated: 2022-06-25Bibliographically approved
Valle-Perez, G., Henter, G. E., Beskow, J., Holzapfel, A., Oudeyer, P.-Y. & Alexanderson, S. (2021). Transflower: probabilistic autoregressive dance generation with multimodal attention. ACM Transactions on Graphics, 40(6), Article ID 195.
Open this publication in new window or tab >>Transflower: probabilistic autoregressive dance generation with multimodal attention
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2021 (English)In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 40, no 6, article id 195Article in journal (Refereed) Published
Abstract [en]

Dance requires skillful composition of complex movements that follow rhythmic, tonal and timbral features of music. Formally, generating dance conditioned on a piece of music can be expressed as a problem of modelling a high-dimensional continuous motion signal, conditioned on an audio signal. In this work we make two contributions to tackle this problem. First, we present a novel probabilistic autoregressive architecture that models the distribution over future poses with a normalizing flow conditioned on previous poses as well as music context, using a multimodal transformer encoder. Second, we introduce the currently largest 3D dance-motion dataset, obtained with a variety of motion-capture technologies, and including both professional and casual dancers. Using this dataset, we compare our new model against two baselines, via objective metrics and a user study, and show that both the ability to model a probability distribution, as well as being able to attend over a large motion and music context are necessary to produce interesting, diverse, and realistic dance that matches the music.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2021
Keywords
Generative models, machine learning, normalising flows, Glow, transformers, dance
National Category
Computer graphics and computer vision Computer Sciences Signal Processing
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-307028 (URN)10.1145/3478513.3480570 (DOI)000729846700001 ()2-s2.0-85125127739 (Scopus ID)
Funder
Swedish Research Council, 2018-05409Swedish Research Council, 2019-03694Knut and Alice Wallenberg Foundation, WASPMarianne and Marcus Wallenberg Foundation, 2020.0102
Note

QC 20220520

Available from: 2022-01-11 Created: 2022-01-11 Last updated: 2025-02-01Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7801-7617

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