Adeyemi Ademola A.Ademola@napier.ac.uk
Research Student
NeFT-Net: N-window extended frequency transformer for rhythmic motion prediction
Ademola, Adeyemi; Sinclair, David; Koniaris, Babis; Hannah, Samantha; Mitchell, Kenny
Authors
David Sinclair
Babis Koniaris
Samantha Hannah
Prof Kenny Mitchell K.Mitchell2@napier.ac.uk
Professor
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.
Citation
Ademola, A., Sinclair, D., Koniaris, B., Hannah, S., & Mitchell, K. (2025). NeFT-Net: N-window extended frequency transformer for rhythmic motion prediction. Computers and Graphics, 129, Article 104244. https://doi.org/10.1016/j.cag.2025.104244
Journal Article Type | Article |
---|---|
Acceptance Date | May 2, 2025 |
Online Publication Date | May 17, 2025 |
Publication Date | 2025-06 |
Deposit Date | Jan 15, 2025 |
Publicly Available Date | Mar 31, 2025 |
Journal | Elsevier Computers and Graphics Special Issue on CGVC |
Print ISSN | 0097-8493 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 129 |
Article Number | 104244 |
DOI | https://doi.org/10.1016/j.cag.2025.104244 |
Keywords | Machine learning, Motion processing, Rendering, Virtual reality |
Public URL | http://researchrepository.napier.ac.uk/Output/4050770 |
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NeFT-Net: N-window Extended Frequency Transformer For Rhythmic Motion Prediction
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Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/
Copyright Statement
CC BY NC 4.0
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