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Updated: Aug 25, 2025

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Shift Pose: A Lightweight Transformer-like Neural Network for Human Pose Estimation.

Haijian Chen1, Xinyun Jiang1, Yonghui Dai2

  • 1The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

ShiftPose is a novel transformer-like model for real-time human pose estimation. It replaces computationally expensive self-attention with a shift operator, achieving high accuracy and speed for human behavior analysis.

Keywords:
human-computer interactionreal-time human pose estimationregression-based approachresidual log-likelihood estimationshift operatortransformer

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Real-time human pose estimation is crucial for understanding human behavior but faces challenges with computational resources and accuracy.
  • Existing transformer models offer high performance but their self-attention mechanisms hinder real-time application due to computational demands.

Purpose of the Study:

  • To develop a computationally efficient and accurate model for real-time human pose detection and tracking.
  • To address the limitations of current models in balancing performance, parameter count, and computational cost for real-time applications.

Main Methods:

  • Proposed ShiftPose, a transformer-like, regression-based model that replaces self-attention with a shift operator.
  • Implemented a bridge-branch connection for multi-resolution integration, enhancing feature fusion.
  • Utilized Simple Disentangled Representation (SimDR) for stable and robust training.

Main Results:

  • Achieved 86.4 PCKH on MPII and 72.2 mAP on COCO datasets.
  • Demonstrated high efficiency with 255 frames per second (fps) on GPU, 10.2M parameters, and 1.6 GFLOPs.
  • Successfully applied to single-stage 3D human pose estimation, yielding exploratory conclusions.

Conclusions:

  • ShiftPose offers a promising new method for high-performance, real-time human pose detection and tracking.
  • The model effectively overcomes computational bottlenecks associated with self-attention mechanisms.
  • The approach provides a viable solution for real-time human behavior analysis and 3D pose estimation.