Iterative Graph Filtering Network for 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2307.16074v2
• Date: Mon, 7 Aug 2023 22:11:33 GMT
• Title: Iterative Graph Filtering Network for 3D Human Pose Estimation
• Authors: Zaedul Islam and A. Ben Hamza
• Abstract summary: Graph convolutional networks (GCNs) have proven to be an effective approach for 3D human pose estimation. In this paper, we introduce an iterative graph filtering framework for 3D human pose estimation. Our approach builds upon the idea of iteratively solving graph filtering with Laplacian regularization.
• Score: 5.177947445379688
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Graph convolutional networks (GCNs) have proven to be an effective approach for 3D human pose estimation. By naturally modeling the skeleton structure of the human body as a graph, GCNs are able to capture the spatial relationships between joints and learn an efficient representation of the underlying pose. However, most GCN-based methods use a shared weight matrix, making it challenging to accurately capture the different and complex relationships between joints. In this paper, we introduce an iterative graph filtering framework for 3D human pose estimation, which aims to predict the 3D joint positions given a set of 2D joint locations in images. Our approach builds upon the idea of iteratively solving graph filtering with Laplacian regularization via the Gauss-Seidel iterative method. Motivated by this iterative solution, we design a Gauss-Seidel network (GS-Net) architecture, which makes use of weight and adjacency modulation, skip connection, and a pure convolutional block with layer normalization. Adjacency modulation facilitates the learning of edges that go beyond the inherent connections of body joints, resulting in an adjusted graph structure that reflects the human skeleton, while skip connections help maintain crucial information from the input layer's initial features as the network depth increases. We evaluate our proposed model on two standard benchmark datasets, and compare it with a comprehensive set of strong baseline methods for 3D human pose estimation. Our experimental results demonstrate that our approach outperforms the baseline methods on both datasets, achieving state-of-the-art performance. Furthermore, we conduct ablation studies to analyze the contributions of different components of our model architecture and show that the skip connection and adjacency modulation help improve the model performance.

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