Hand pose estimation based on regression method from monocular RGB cameras for handling occlusion

Author:

Roumaissa Bekiri,Mohamed Chaouki Babahenini

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference48 articles.

1. Athitsos, V., Sclaroff, S.: Estimating 3d hand pose from a cluttered image. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., vol. 2, p. 432 (2003). IEEE

2. Baek, S., Kim, K.I., Kim, T.-K.: Pushing the envelope for rgb-based dense 3d hand pose estimation via neural rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1067–1076 (2019)

3. Boukhayma, A., Bem, R.d., Torr, P.H.: 3d hand shape and pose from images in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10843–10852 (2019)

4. Cai, Y., Ge, L., Cai, J., Yuan, J.: Weakly-supervised 3d hand pose estimation from monocular rgb images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 666–682 (2018)

5. Chen, L., Lin, S.-Y., Xie, Y., Tang, H., Xue, Y., Xie, X., Lin, Y.-Y., Fan, W.: Generating realistic training images based on tonality-alignment generative adversarial networks for hand pose estimation. arXiv preprint arXiv:1811.09916 (2018)

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