AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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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).

2024

Rezaei, E.; Mosallanezhad, B.

Identifying social concerns in virtual reality technology through text mining and large language models, and prioritizing them with the fuzzy hierarchized analytic network process Journal Article

In: Computers and Electrical Engineering, vol. 120, 2024, ISSN: 00457906 (ISSN).

Abstract | Links | BibTeX | Tags: Analytic network, Generative adversarial networks, Hierarchized analytic network process, Language Model, Large language model, large language models, Latent Semantic Analysis, Multi attribute decision making, Multi criteria decision-making, Multi-attribute decision making, Multi-criteria decision making, Multicriteria decision-making, Multicriterion decision makings, Network process, Text mining, Text-mining, Virtual environments, Virtual Reality

@article{rezaei_identifying_2024,

title = {Identifying social concerns in virtual reality technology through text mining and large language models, and prioritizing them with the fuzzy hierarchized analytic network process},

author = {E. Rezaei and B. Mosallanezhad},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209371667&doi=10.1016%2fj.compeleceng.2024.109770&partnerID=40&md5=027fa69a158351e413da655b98c60999},

doi = {10.1016/j.compeleceng.2024.109770},

issn = {00457906 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Computers and Electrical Engineering},

volume = {120},

abstract = {Virtual reality technology has rapidly gained popularity as an entertainment medium, drawing interest from diverse age groups. However, its widespread adoption depends on effectively addressing public concerns and achieving market acceptance. While some studies have acknowledged these concerns, a significant gap persists in comprehensive research that incorporates both individual and expert perspectives. Consequently, certain underlying social issues related to virtual reality systems remain unexplored and unprioritized. To address this gap, this paper proposes a methodology that utilizes Latent Semantic Analysis (LSA) to identify and assess social concerns from various sources, including user perspectives. Large Language Models (LLMs) assist in retrieving relevant chunks of articles during analysis, enhancing data quality. Furthermore, we introduce a novel decision-making tool, the Hierarchized Analytic Network Process (HANP) and its fuzzy form, to effectively rank these concerns. This approach addresses a limitation of the traditional Analytic Network Process (ANP), which can overemphasize dependent attributes, potentially leading to zero-weighted, less important attributes and making comparisons impossible. By prioritizing social concerns based on their significance, our approach aims to facilitate broader social acceptance of virtual reality technologies among the general public. To further demonstrate the advantages of our proposed approach, the results obtained from F-HANP (in situations where fuzzy judgments are available) and HANP are compared with other popular decision-making methods. © 2024 Elsevier Ltd},

keywords = {Analytic network, Generative adversarial networks, Hierarchized analytic network process, Language Model, Large language model, large language models, Latent Semantic Analysis, Multi attribute decision making, Multi criteria decision-making, Multi-attribute decision making, Multi-criteria decision making, Multicriteria decision-making, Multicriterion decision makings, Network process, Text mining, Text-mining, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}