AHCI RESEARCH GROUP

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OUR RESEARCH

Scientific Publications

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2025

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}

}