STGFormer: Spatio-Temporal GraphFormer for 3D Human Pose Estimation in Video

• URL: http://arxiv.org/abs/2407.10099v1
• Date: Sun, 14 Jul 2024 06:45:27 GMT
• Title: STGFormer: Spatio-Temporal GraphFormer for 3D Human Pose Estimation in Video
• Authors: Yang Liu, Zhiyong Zhang,
• Abstract summary: This paper presents a graph-based framework for 3D human pose estimation in video. Specifically, we develop a graph-based attention mechanism, integrating graph information directly into the respective attention layers. We demonstrate that our method achieves significant stateof-the-art performance in 3D human pose estimation.
• Score: 7.345621536750547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The current methods of video-based 3D human pose estimation have achieved significant progress; however, they continue to confront the significant challenge of depth ambiguity. To address this limitation, this paper presents the spatio-temporal GraphFormer framework for 3D human pose estimation in video, which integrates body structure graph-based representations with spatio-temporal information. Specifically, we develop a spatio-temporal criss-cross graph (STG) attention mechanism. This approach is designed to learn the long-range dependencies in data across both time and space, integrating graph information directly into the respective attention layers. Furthermore, we introduce the dual-path modulated hop-wise regular GCN (MHR-GCN) module, which utilizes modulation to optimize parameter usage and employs spatio-temporal hop-wise skip connections to acquire higher-order information. Additionally, this module processes temporal and spatial dimensions independently to learn their respective features while avoiding mutual influence. Finally, we demonstrate that our method achieves state-of-the-art performance in 3D human pose estimation on the Human3.6M and MPI-INF-3DHP datasets.

Related papers

• Graph and Skipped Transformer: Exploiting Spatial and Temporal Modeling Capacities for Efficient 3D Human Pose Estimation [36.93661496405653]
  We take a global approach to exploit Transformer-temporal information with a concise Graph and Skipped Transformer architecture. Specifically, in 3D pose stage, coarse-grained body parts are deployed to construct a fully data-driven adaptive model. Experiments are conducted on Human3.6M, MPI-INF-3DHP and Human-Eva benchmarks.
  arXiv  Detail & Related papers  (2024-07-03T10:42:09Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling [83.76377808476039]
  We propose a new modeling method for human pose deformations and design an accompanying diffusion-based motion prior. Inspired by the field of non-rigid structure-from-motion, we divide the task of reconstructing 3D human skeletons in motion into the estimation of a 3D reference skeleton. A mixed spatial-temporal NRSfMformer is used to simultaneously estimate the 3D reference skeleton and the skeleton deformation of each frame from 2D observations sequence.
  arXiv  Detail & Related papers  (2023-08-18T16:41:57Z)
• 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)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• Self-Attentive 3D Human Pose and Shape Estimation from Videos [82.63503361008607]
  We present a video-based learning algorithm for 3D human pose and shape estimation. We exploit temporal information in videos and propose a self-attention module. We evaluate our method on the 3DPW, MPI-INF-3DHP, and Human3.6M datasets.
  arXiv  Detail & Related papers  (2021-03-26T00:02:19Z)
• Enhanced 3D Human Pose Estimation from Videos by using Attention-Based Neural Network with Dilated Convolutions [12.900524511984798]
  We show a systematic design for how conventional networks and other forms of constraints can be incorporated into the attention framework. We achieve this by adapting temporal receptive field via a multi-scale structure of dilated convolutions. Our method achieves the state-of-the-art performance and outperforms existing methods by reducing the mean per joint position error to 33.4 mm on Human3.6M dataset.
  arXiv  Detail & Related papers  (2021-03-04T17:26:51Z)
• 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)
• A Graph Attention Spatio-temporal Convolutional Network for 3D Human Pose Estimation in Video [7.647599484103065]
  We improve the learning of constraints in human skeleton by modeling local global spatial information via attention mechanisms. Our approach effectively mitigates depth ambiguity and self-occlusion, generalizes to half upper body estimation, and achieves competitive performance on 2D-to-3D video pose estimation.
  arXiv  Detail & Related papers  (2020-03-11T14:54:40Z)

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.