AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Zhou, J.; Weber, R.; Wen, E.; Lottridge, D.
Real-Time Full-body Interaction with AI Dance Models: Responsiveness to Contemporary Dance Proceedings Article
In: Int Conf Intell User Interfaces Proc IUI, pp. 1177–1187, Association for Computing Machinery, 2025, ISBN: 979-840071306-4 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, Chatbots, Computer interaction, Deep learning, Deep-Learning Dance Model, Design of Human-Computer Interaction, Digital elevation model, Generative AI, Input output programs, Input sequence, Interactivity, Motion capture, Motion tracking, Movement analysis, Output sequences, Problem oriented languages, Real- time, Text mining, Three dimensional computer graphics, User input, Virtual environments, Virtual Reality
@inproceedings{zhou_real-time_2025,
title = {Real-Time Full-body Interaction with AI Dance Models: Responsiveness to Contemporary Dance},
author = {J. Zhou and R. Weber and E. Wen and D. Lottridge},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001922427&doi=10.1145%2f3708359.3712077&partnerID=40&md5=cea9213198220480b80b7a4840d26ccc},
doi = {10.1145/3708359.3712077},
isbn = {979-840071306-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int Conf Intell User Interfaces Proc IUI},
pages = {1177–1187},
publisher = {Association for Computing Machinery},
abstract = {Interactive AI chatbots put the power of Large-Language Models (LLMs) into people's hands; it is this interactivity that fueled explosive worldwide influence. In the generative dance space, however, there are few deep-learning-based generative dance models built with interactivity in mind. The release of the AIST++ dance dataset in 2021 led to an uptick of capabilities in generative dance models. Whether these models could be adapted to support interactivity and how well this approach will work is not known. In this study, we explore the capabilities of existing generative dance models for motion-to-motion synthesis on real-time, full-body motion-captured contemporary dance data. We identify an existing model that we adapted to support interactivity: the Bailando++ model, which is trained on the AIST++ dataset and was modified to take music and a motion sequence as input parameters in an interactive loop. We worked with two professional contemporary choreographers and dancers to record and curate a diverse set of 203 motion-captured dance sequences as a set of "user inputs"captured through the Optitrack high-precision motion capture 3D tracking system. We extracted 17 quantitative movement features from the motion data using the well-established Laban Movement Analysis theory, which allowed for quantitative comparisons of inter-movement correlations, which we used for clustering input data and comparing input and output sequences. A total of 10 pieces of music were used to generate a variety of outputs using the adapted Bailando++ model. We found that, on average, the generated output motion achieved only moderate correlations to the user input, with some exceptions of movement and music pairs achieving high correlation. The high-correlation generated output sequences were deemed responsive and relevant co-creations in relation to the input sequences. We discuss implications for interactive generative dance agents, where the use of 3D joint coordinate data should be used over SMPL parameters for ease of real-time generation, and how the use of Laban Movement Analysis could be used to extract useful features and fine-tune deep-learning models. © 2025 Copyright held by the owner/author(s).},
keywords = {3D modeling, Chatbots, Computer interaction, Deep learning, Deep-Learning Dance Model, Design of Human-Computer Interaction, Digital elevation model, Generative AI, Input output programs, Input sequence, Interactivity, Motion capture, Motion tracking, Movement analysis, Output sequences, Problem oriented languages, Real- time, Text mining, Three dimensional computer graphics, User input, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Interactive AI chatbots put the power of Large-Language Models (LLMs) into people's hands; it is this interactivity that fueled explosive worldwide influence. In the generative dance space, however, there are few deep-learning-based generative dance models built with interactivity in mind. The release of the AIST++ dance dataset in 2021 led to an uptick of capabilities in generative dance models. Whether these models could be adapted to support interactivity and how well this approach will work is not known. In this study, we explore the capabilities of existing generative dance models for motion-to-motion synthesis on real-time, full-body motion-captured contemporary dance data. We identify an existing model that we adapted to support interactivity: the Bailando++ model, which is trained on the AIST++ dataset and was modified to take music and a motion sequence as input parameters in an interactive loop. We worked with two professional contemporary choreographers and dancers to record and curate a diverse set of 203 motion-captured dance sequences as a set of "user inputs"captured through the Optitrack high-precision motion capture 3D tracking system. We extracted 17 quantitative movement features from the motion data using the well-established Laban Movement Analysis theory, which allowed for quantitative comparisons of inter-movement correlations, which we used for clustering input data and comparing input and output sequences. A total of 10 pieces of music were used to generate a variety of outputs using the adapted Bailando++ model. We found that, on average, the generated output motion achieved only moderate correlations to the user input, with some exceptions of movement and music pairs achieving high correlation. The high-correlation generated output sequences were deemed responsive and relevant co-creations in relation to the input sequences. We discuss implications for interactive generative dance agents, where the use of 3D joint coordinate data should be used over SMPL parameters for ease of real-time generation, and how the use of Laban Movement Analysis could be used to extract useful features and fine-tune deep-learning models. © 2025 Copyright held by the owner/author(s).
Ademola, A.; Sinclair, D.; Koniaris, B.; Hannah, S.; Mitchell, K.
NeFT-Net: N-window extended frequency transformer for rhythmic motion prediction Journal Article
In: Computers and Graphics, vol. 129, 2025, ISSN: 00978493 (ISSN).
Abstract | Links | BibTeX | Tags: Cosine transforms, Discrete cosine transforms, Human motions, Immersive, machine learning, Machine-learning, Motion analysis, Motion prediction, Motion processing, Motion sequences, Motion tracking, Real-world, Rendering, Rendering (computer graphics), Rhythmic motion, Three dimensional computer graphics, Virtual environments, Virtual Reality
@article{ademola_neft-net_2025,
title = {NeFT-Net: N-window extended frequency transformer for rhythmic motion prediction},
author = {A. Ademola and D. Sinclair and B. Koniaris and S. Hannah and K. Mitchell},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006724723&doi=10.1016%2fj.cag.2025.104244&partnerID=40&md5=08fd0792837332404ec9acdd16f608bf},
doi = {10.1016/j.cag.2025.104244},
issn = {00978493 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Computers and Graphics},
volume = {129},
abstract = {Advancements in prediction of human motion sequences are critical for enabling online virtual reality (VR) users to dance and move in ways that accurately mirror real-world actions, delivering a more immersive and connected experience. However, latency in networked motion tracking remains a significant challenge, disrupting engagement and necessitating predictive solutions to achieve real-time synchronization of remote motions. To address this issue, we propose a novel approach leveraging a synthetically generated dataset based on supervised foot anchor placement timings for rhythmic motions, ensuring periodicity and reducing prediction errors. Our model integrates a discrete cosine transform (DCT) to encode motion, refine high-frequency components, and smooth motion sequences, mitigating jittery artifacts. Additionally, we introduce a feed-forward attention mechanism designed to learn from N-window pairs of 3D key-point pose histories for precise future motion prediction. Quantitative and qualitative evaluations on the Human3.6M dataset highlight significant improvements in mean per joint position error (MPJPE) metrics, demonstrating the superiority of our technique over state-of-the-art approaches. We further introduce novel result pose visualizations through the use of generative AI methods. © 2025 The Authors},
keywords = {Cosine transforms, Discrete cosine transforms, Human motions, Immersive, machine learning, Machine-learning, Motion analysis, Motion prediction, Motion processing, Motion sequences, Motion tracking, Real-world, Rendering, Rendering (computer graphics), Rhythmic motion, Three dimensional computer graphics, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Advancements in prediction of human motion sequences are critical for enabling online virtual reality (VR) users to dance and move in ways that accurately mirror real-world actions, delivering a more immersive and connected experience. However, latency in networked motion tracking remains a significant challenge, disrupting engagement and necessitating predictive solutions to achieve real-time synchronization of remote motions. To address this issue, we propose a novel approach leveraging a synthetically generated dataset based on supervised foot anchor placement timings for rhythmic motions, ensuring periodicity and reducing prediction errors. Our model integrates a discrete cosine transform (DCT) to encode motion, refine high-frequency components, and smooth motion sequences, mitigating jittery artifacts. Additionally, we introduce a feed-forward attention mechanism designed to learn from N-window pairs of 3D key-point pose histories for precise future motion prediction. Quantitative and qualitative evaluations on the Human3.6M dataset highlight significant improvements in mean per joint position error (MPJPE) metrics, demonstrating the superiority of our technique over state-of-the-art approaches. We further introduce novel result pose visualizations through the use of generative AI methods. © 2025 The Authors