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November 4, 2025Concurrency and Computation Practice and ExperienceOpen Access

KANs ‐ GCN Transformer With Hybrid Adjacency Graphs for Accurate 3D Human Pose Estimation

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Achieving a P1 error of 41.5 mm and P2 error of 32.6 mm demonstrates significant improvement.
Using a dynamic and static adjacency matrix allows for adaptive feature-based connections in the model.
The innovative framework integrates Chebyshev polynomial convolution to enhance graph convolution capabilities.
Supports robust modeling of human poses in complex environments, highlighting its potential in fitness and gaming.

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Implication

Observational analysis shows improved accuracy in human pose estimation, suggesting a novel approach using adjacency matrix in GCN and Transformer frameworks.

Cite This Study

pan et al. (2025) studied this question.

synapsesocial.com/papers/690945348f2297dc13532e9f https://doi.org/10.1002/cpe.70403

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Implication

Observational analysis shows improved accuracy in human pose estimation, suggesting a novel approach using adjacency matrix in GCN and Transformer frameworks.

KANs ‐ GCN Transformer With Hybrid Adjacency Graphs for Accurate 3D Human Pose Estimation

Key Points

Discussion

Implication

Cite This Study

KANs ‐ GCN Transformer With Hybrid Adjacency Graphs for Accurate 3D Human Pose Estimation

Key Points

Discussion

Implication

Cite This Study

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