Dynamic Differencing-Based Hybrid Network for Improved 3D Skeleton-Based Motion Prediction
<b>Background:</b> Three-dimensional skeleton-based human motion prediction is an essential and challenging task for human–machine interactions, aiming to forecast future poses given a history of previous motions. However, existing methods often fail to effectively model dynamic changes...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | AI |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-2688/5/4/139 |
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| Summary: | <b>Background:</b> Three-dimensional skeleton-based human motion prediction is an essential and challenging task for human–machine interactions, aiming to forecast future poses given a history of previous motions. However, existing methods often fail to effectively model dynamic changes and optimize spatial–temporal features. <b>Methods:</b> In this paper, we introduce Dynamic Differencing-based Hybrid Networks (2DHnet), which addresses these issues with two innovations: the Dynamic Differential Dependencies Extractor (2D-DE) for capturing dynamic features like velocity and acceleration, and the Attention-based Spatial–Temporal Dependencies Extractor (AST-DE) for enhancing spatial–temporal correlations. The 2DHnet combines these into a dual-branch network, offering a comprehensive motion representation. <b>Results:</b> Experiments on the Human3.6M and 3DPW datasets show that 2DHnet significantly outperforms existing methods, with average improvements of 4.7% and 26.6% in MPJPE, respectively. |
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| ISSN: | 2673-2688 |