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Multimodal Art Pose Recognition and Interaction With Human Intelligence Enhancement.

Chengming Ma1, Qian Liu1, Yaqi Dang1

  • 1College of Communication, Northwest Normal University, Lanzhou, China.

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Summary

This study introduces a multimodal network for artistic pose recognition, fusing RGB and depth data for robust analysis. It achieves 99.04% accuracy in real-time hand gesture recognition for virtual-reality fusion.

Keywords:
human ARTintelligent augmentationinteractionmultimodalitypose recognition

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Human artistic pose recognition is crucial for applications like animation and virtual reality.
  • Existing methods often struggle with variations in lighting, viewpoint, and complex temporal structures.

Purpose of the Study:

  • To develop an enhanced multimodal artistic pose recognition system.
  • To improve the robustness and real-time interaction in virtual-reality fusion for hand gestures.

Main Methods:

  • A complementary network architecture fusing RGB and depth data using motion energy.
  • An energy-guided video segmentation method for modeling long-range temporal structures.
  • A cross-modal cross-fusion approach for sharing local and global features.

Main Results:

  • The system effectively utilizes appearance and depth information for pose recognition.
  • Achieved an average correct rate of 99.04% in real-time hand gesture recognition for VR fusion.
  • Demonstrated improved robustness and real-time interaction in augmented reality scenarios.

Conclusions:

  • The proposed multimodal approach significantly enhances artistic pose recognition.
  • The developed coordinate consistency model enables seamless virtual-reality fusion.
  • This work advances the capabilities of human-computer interaction through accurate and responsive gesture recognition.