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MetaAnimNet: Transformer-Based Cross-Platform Character Animation Intelligence for Metaverse Applications
Authors
C. Vidyanand
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Keywords
transformer-based animation; metaverse character animation; cross-platform motion retargeting; temporal attention mechanism; avatar synchronization; real-time motion synthesis; animation intelligence; deep learning motion prediction
Abstract
Persistent challenges in real-time character animation across heterogeneous metaverse platforms have motivated the development of architecturally sophisticated AI systems capable of unified motion synthesis and cross-device deployment. This paper presents MetaAnimNet, a transformer-based animation intelligence framework designed to address the longstanding disconnect between motion quality, computational efficiency, and platform interoperability in virtual environment applications. The proposed architecture integrates a temporal attention mechanism with a motion context fusion module, enabling the system to generate fluid, physically plausible character animations that adapt to varying skeletal rigs and rendering pipelines across Unity, Unreal Engine, and web-based environments. Unlike prior deep learning approaches that optimize for a single platform configuration, MetaAnimNet employs a cloud-edge synchronization strategy that balances inference load between edge devices and server-side computation, sustaining real-time frame rates even under constrained hardware conditions. Experimental evaluation across four benchmark datasets, including Human3.6M, CMU Motion Capture, Mixamo, and AMASS, demonstrates that MetaAnimNet achieves a mean per-joint position error reduction of 18.4% over the strongest baseline, while maintaining an average inference latency of 23.7 milliseconds per frame on standard consumer-grade GPUs. Motion smoothness scores and cross-platform consistency metrics further confirm the practical deployability of the proposed system. The framework is validated in a live metaverse testbed, where avatar synchronization accuracy and user immersion quality are measured against existing animation intelligence systems.
IJCRT's Publication Details
Unique Identification Number - IJCRT2410693
Paper ID - 309592
Author type - Indian Author
Page Number(s) - f971-f982
Pubished in - Volume 12 | Issue 10 | October 2024
DOI (Digital Object Identifier) - https://doi.org/10.56975/ijcrt.v12i10.309592
No Of Downloads - 30
Author Country - India, -, -, -, -, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Published Paper PDF : - http://www.ijcrt.org/papers/IJCRT2410693
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C. Vidyanand, "MetaAnimNet: Transformer-Based Cross-Platform Character Animation Intelligence for Metaverse Applications", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 10, pp.f971-f982, October 2024, Available at :http://www.ijcrt.org/papers/IJCRT2410693.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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