HDFormer: High-order Directed Transformer for 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2302.01825v2
• Date: Mon, 22 May 2023 06:32:17 GMT
• Title: HDFormer: High-order Directed Transformer for 3D Human Pose Estimation
• Authors: Hanyuan Chen, Jun-Yan He, Wangmeng Xiang, Zhi-Qi Cheng, Wei Liu, Hanbing Liu, Bin Luo, Yifeng Geng, Xuansong Xie
• Abstract summary: HDFormer significantly outperforms state-of-the-art (SOTA) models on Human3.6M and MPI-INF-3DHP datasets. HDFormer demonstrates broad real-world applicability, enabling real-time, accurate 3D pose estimation.
• Score: 20.386530242069338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human pose estimation is a challenging task due to its structured data sequence nature. Existing methods primarily focus on pair-wise interaction of body joints, which is insufficient for scenarios involving overlapping joints and rapidly changing poses. To overcome these issues, we introduce a novel approach, the High-order Directed Transformer (HDFormer), which leverages high-order bone and joint relationships for improved pose estimation. Specifically, HDFormer incorporates both self-attention and high-order attention to formulate a multi-order attention module. This module facilitates first-order "joint$\leftrightarrow$joint", second-order "bone$\leftrightarrow$joint", and high-order "hyperbone$\leftrightarrow$joint" interactions, effectively addressing issues in complex and occlusion-heavy situations. In addition, modern CNN techniques are integrated into the transformer-based architecture, balancing the trade-off between performance and efficiency. HDFormer significantly outperforms state-of-the-art (SOTA) models on Human3.6M and MPI-INF-3DHP datasets, requiring only 1/10 of the parameters and significantly lower computational costs. Moreover, HDFormer demonstrates broad real-world applicability, enabling real-time, accurate 3D pose estimation. The source code is in https://github.com/hyer/HDFormer

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