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Future Pose Prediction from 3D Human Skeleton Sequence with Surrounding Situation.

Tomohiro Fujita1, Yasutomo Kawanishi1

  • 1Guardian Robot Project R-IH, RIKEN, Advanced Telecommunications Research Institute International, 3rd Floor, 2-2-2 Hikaridai, Seika-cho, Sorakugun, Kyoto 619-0288, Japan.

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Summary
This summary is machine-generated.

Predicting future human poses for robots is challenging due to motion variety. This study incorporates surrounding environmental context, like objects and people, into human pose prediction models, significantly improving accuracy for object- and human-related movements.

Keywords:
3D skeleton sequencepose predictionsurrounding information

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Human pose prediction is crucial for human-robot interaction and robot control.
  • Current deep learning methods predict future poses from 3D skeleton sequences, but struggle with motion variability.
  • Human movements are influenced by surrounding objects and individuals, a factor often overlooked.

Purpose of the Study:

  • To develop a novel method for human pose prediction that integrates contextual environmental information.
  • To improve the accuracy of predicting future human skeleton sequences by considering the surrounding situation.

Main Methods:

  • Proposed a prediction model that incorporates surrounding environmental features extracted from images.
  • Utilized image features around the target person as contextual information for pose prediction.
  • Evaluated the method on publicly available datasets.

Main Results:

  • The proposed method demonstrated improved prediction accuracy compared to existing approaches.
  • Performance gains were particularly notable for motions involving interactions with objects and other humans.
  • Incorporating surrounding context effectively addresses the inherent variability in human motion prediction.

Conclusions:

  • Considering the surrounding environment is a key factor in enhancing human pose prediction accuracy.
  • The developed method offers a more robust approach to predicting future human movements for robotic applications.
  • This research advances the capabilities of robots in understanding and interacting with dynamic human environments.