PyCAT4: A Hierarchical Vision Transformer-based Framework for 3D Human Pose Estimation

TL;DR

PyCAT4 addresses the challenge of accurate and real-time $3D$ human pose estimation by integrating a Swin Transformer backbone, Coordinate Attention, multi-scale fusion (FPN+ASPP), and a temporal fusion module into the PyMAF framework. It demonstrates that self-attention improves spatial encoding, temporal fusion enhances video-based coherence, and multi-scale fusion balances representations across scales, achieving improved results on the COCO and 3DPW benchmarks and enabling a real-time system. An extensive ablation study across CA, Swin, FPN+ASPP, and temporal components highlights their individual and combined contributions. The work advances practical $3D$ pose estimation for real-world applications such as AR/VR, sports analytics, and human-computer interaction, while also outlining ethical considerations and directions for future research.

Abstract

Recently, a significant improvement in the accuracy of 3D human pose estimation has been achieved by combining convolutional neural networks (CNNs) with pyramid grid alignment feedback loops. Additionally, innovative breakthroughs have been made in the field of computer vision through the adoption of Transformer-based temporal analysis architectures. Given these advancements, this study aims to deeply optimize and improve the existing Pymaf network architecture. The main innovations of this paper include: (1) Introducing a Transformer feature extraction network layer based on self-attention mechanisms to enhance the capture of low-level features; (2) Enhancing the understanding and capture of temporal signals in video sequences through feature temporal fusion techniques; (3) Implementing spatial pyramid structures to achieve multi-scale feature fusion, effectively balancing feature representations differences across different scales. The new PyCAT4 model obtained in this study is validated through experiments on the COCO and 3DPW datasets. The results demonstrate that the proposed improvement strategies significantly enhance the network's detection capability in human pose estimation, further advancing the development of human pose estimation technology.

Figures (20)

• Figure 1: Classification of Human Pose Estimation
• Figure 2: 2D Human Pose Estimation wang2020deep
• Figure 3: 3D Human Pose Estimation
• Figure 4: Illustration of the regression-based human pose estimation method zheng2023deep.
• Figure 5: Illustration of the heatmap-based method zheng2023deep.