Flexible Graph Convolutional Network for 3D Human Pose Estimation


Abu Taib Mohammed Shahjahan (Concordia University), Abdessamad Ben Hamza (Concordia University)
The 35th British Machine Vision Conference
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Abstract

Although graph convolutional networks exhibit promising performance in 3D human pose estimation, their reliance on one-hop neighbors limits their ability to capture high-order dependencies among body joints, crucial for mitigating uncertainty arising from occlusion or depth ambiguity. To tackle this limitation, we introduce Flex-GCN, a flexible graph convolutional network designed to learn graph representations that capture broader global information and dependencies. At its core is the flexible graph convolution, which aggregates features from both immediate and second-order neighbors of each node, while maintaining the same time and memory complexity as the standard convolution. Our network architecture comprises residual blocks of flexible graph convolutional layers, as well as a global response normalization layer for global feature aggregation, normalization and calibration. Quantitative and qualitative results demonstrate the effectiveness of our model, achieving competitive performance on benchmark datasets. Code is available at: https://github.com/shahjahan0275/Flex-GCN

Citation

@inproceedings{Shahjahan_2024_BMVC,
author    = {Abu Taib Mohammed Shahjahan and Abdessamad Ben Hamza},
title     = {Flexible Graph Convolutional Network for 3D Human Pose Estimation},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0769.pdf}
}


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