Skeletor: Skeletal Transformers for Robust Body-Pose Estimation

@article{Jiang2021SkeletorST,
  title={Skeletor: Skeletal Transformers for Robust Body-Pose Estimation},
  author={Tao Jiang and Necati Cihan Camgoz and R. Bowden},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2021},
  pages={3389-3397}
}
  • Tao Jiang, N. C. Camgoz, R. Bowden
  • Published 23 April 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Predicting 3D human pose from a single monoscopic video can be highly challenging due to factors such as low resolution, motion blur and occlusion, in addition to the fundamental ambiguity in estimating 3D from 2D. Approaches that directly regress the 3D pose from independent images can be particularly susceptible to these factors and result in jitter, noise and/or inconsistencies in skeletal estimation. Much of which can be overcome if the temporal evolution of the scene and skeleton are taken… Expand

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