Implicit Neural Representations for Variable Length Human Motion Generation-Reference-Cited by-同舟云学术

Implicit Neural Representations for Variable Length Human Motion Generation

Published:2022 Issue: Volume: Page:356-372
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Cervantes Pablo^ORCID,Sekikawa Yusuke^ORCID,Sato Ikuro^ORCID,Shinoda Koichi^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-19790-1_22

Reference42 articles.

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2. Barsoum, E., Kender, J., Liu, Z.: HP-GAN: probabilistic 3D human motion prediction via GAN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1418–1427 (2018)

3. Battan, N., Agrawal, Y., Rao, S.S., Goel, A., Sharma, A.: GlocalNet: class-aware long-term human motion synthesis. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 879–888 (2021)

4. Bond-Taylor, S., Willcocks, C.G.: Gradient origin networks. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3–7 May 2021. OpenReview.net (2021)

5. Butepage, J., Black, M.J., Kragic, D., Kjellstrom, H.: Deep representation learning for human motion prediction and classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6158–6166 (2017)

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