UKPGAN: A General Self-Supervised Keypoint Detector

Published in CVPR, 2022

Yang You, Wenhai Liu, Yanjie Ze, Yong-Lu Li, Weiming Wang, Cewu Lu

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Abstract

In this work, we reckon keypoints under an information compression scheme to represent the whole object. Based on this, we propose UKPGAN, an unsupervised 3D keypoint detector where keypoints are detected so that they could reconstruct the original object shape. Two modules: GAN-based keypoint sparsity control and salient information distillation modules are proposed to locate those important keypoints. Extensive experiments show that our keypoints preserve the semantic information of objects and align well with human annotated part and keypoint labels.

BibTeX

@inproceedings{you2022ukpgan,
  title     = {UKPGAN: A General Self-Supervised Keypoint Detector},
  author    = {Yang You and Wenhai Liu and Yanjie Ze and Yong-Lu Li and Weiming Wang and Cewu Lu},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2022},
  eprint    = {2011.11974},
  archivePrefix = {arXiv}
}

Recommended citation: You, Y., Liu, W., Ze, Y., Li, Y. L., Wang, W., & Lu, C. (2022). UKPGAN: A General Self-Supervised Keypoint Detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 17042–17051).