Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2506.02853v1
• Date: Tue, 03 Jun 2025 13:21:37 GMT
• Title: Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
• Authors: Mingjie Wei, Xuemei Xie, Yutong Zhong, Guangming Shi,
• Abstract summary: Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose.<n>We propose a novel Pyramid Graph Attention (PGA) module to capture long-range cross-scale dependencies.<n>We also develop a Pyramid Graph Transformer (PGFormer) for 3D human pose estimation, which is a lightweight multi-scale transformer architecture.
• Score: 28.929345360469807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose. Usually, action coordination is represented as a long-range dependence among body parts. However, there are two main challenges in modeling long-range dependencies. First, joints should not only be constrained by other individual joints but also be modulated by the body parts. Second, existing methods make networks deeper to learn dependencies between non-linked parts. They introduce uncorrelated noise and increase the model size. In this paper, we utilize a pyramid structure to better learn potential long-range dependencies. It can capture the correlation across joints and groups, which complements the context of the human sub-structure. In an effective cross-scale way, it captures the pyramid-structured long-range dependence. Specifically, we propose a novel Pyramid Graph Attention (PGA) module to capture long-range cross-scale dependencies. It concatenates information from various scales into a compact sequence, and then computes the correlation between scales in parallel. Combining PGA with graph convolution modules, we develop a Pyramid Graph Transformer (PGFormer) for 3D human pose estimation, which is a lightweight multi-scale transformer architecture. It encapsulates human sub-structures into self-attention by pooling. Extensive experiments show that our approach achieves lower error and smaller model size than state-of-the-art methods on Human3.6M and MPI-INF-3DHP datasets. The code is available at https://github.com/MingjieWe/PGFormer.

Related papers

• PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation [5.223657684081615]
  Existing monocular 3D pose estimation methods rely on joint positional features, while overlooking intrinsic directional and angular correlations within the skeleton.<n>We propose the PoseGRAF framework to address these challenges.<n> Experimental results on the Human3.6M and MPI-INF-3DHP datasets show that our method exceeds state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-06-17T14:59:56Z)
• Double-chain Constraints for 3D Human Pose Estimation in Images and Videos [21.42410292863492]
  Reconstructing 3D poses from 2D poses lacking depth information is challenging due to the complexity and diversity of human motion. We propose a novel model, called Double-chain Graph Convolutional Transformer (DC-GCT), to constrain the pose. We show that DC-GCT achieves state-of-the-art performance on two challenging datasets.
  arXiv  Detail & Related papers  (2023-08-10T02:41:18Z)
• Iterative Graph Filtering Network for 3D Human Pose Estimation [5.177947445379688]
  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.
  arXiv  Detail & Related papers  (2023-07-29T20:46:44Z)
• Regular Splitting Graph Network for 3D Human Pose Estimation [5.177947445379688]
  We introduce a higher-order regular splitting graph network (RS-Net) for 2D-to-3D human pose estimation. Our model achieves superior performance over recent state-of-the-art methods for 3D human pose estimation.
  arXiv  Detail & Related papers  (2023-05-09T22:13:04Z)
• Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimation [53.04781510348416]
  Video-based 3D human pose and shape estimations are evaluated by intra-frame accuracy and inter-frame smoothness. We propose to structurally decouple the modeling of long-term and short-term correlations in an end-to-end framework, Global-to-Local Transformer (GLoT) Our GLoT surpasses previous state-of-the-art methods with the lowest model parameters on popular benchmarks, i.e., 3DPW, MPI-INF-3DHP, and Human3.6M.
  arXiv  Detail & Related papers  (2023-03-26T14:57:49Z)
• Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats [80.12253291709673]
  We propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model.
  arXiv  Detail & Related papers  (2022-12-29T22:22:49Z)
• CrossFormer: Cross Spatio-Temporal Transformer for 3D Human Pose Estimation [24.08170512746056]
  3D human pose estimation can be handled by encoding the geometric dependencies between the body parts and enforcing the kinematic constraints. Recent Transformer has been adopted to encode the long-range dependencies between the joints in the spatial and temporal domains. We propose a novel pose estimation Transformer featuring rich representations of body joints critical for capturing subtle changes across frames.
  arXiv  Detail & Related papers  (2022-03-24T23:40:11Z)
• Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images [79.70127290464514]
  We decompose the task into two stages, i.e. person localization and pose estimation. And we propose three task-specific graph neural networks for effective message passing. Our approach achieves state-of-the-art performance on CMU Panoptic and Shelf datasets.
  arXiv  Detail & Related papers  (2021-09-13T11:44:07Z)
• NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go [109.88509362837475]
  We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes. NeuroMorph produces smooth and point-to-point correspondences between them. It works well for a large variety of input shapes, including non-isometric pairs from different object categories.
  arXiv  Detail & Related papers  (2021-06-17T12:25:44Z)
• A hybrid classification-regression approach for 3D hand pose estimation using graph convolutional networks [1.0152838128195467]
  We propose a two-stage GCN-based framework that learns per-pose relationship constraints. The first phase quantizes the 2D/3D space to classify the joints into 2D/3D blocks based on their locality. The second stage uses a GCN-based module that uses an adaptative nearest neighbor algorithm to determine joint relationships.
  arXiv  Detail & Related papers  (2021-05-23T10:09:10Z)
• HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation [54.23770284299979]
  This paper introduces a novel form of supervision - Hierarchical Multi-person Ordinal Relations (HMOR) HMOR encodes interaction information as the ordinal relations of depths and angles hierarchically. An integrated top-down model is designed to leverage these ordinal relations in the learning process. The proposed method significantly outperforms state-of-the-art methods on publicly available multi-person 3D pose datasets.
  arXiv  Detail & Related papers  (2020-08-01T07:53:27Z)
• Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition [79.33539539956186]
  We propose a simple method to disentangle multi-scale graph convolutions and a unified spatial-temporal graph convolutional operator named G3D. By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets.
  arXiv  Detail & Related papers  (2020-03-31T11:28:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.