TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis-Reference-Cited by-同舟云学术

TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis

Published:2024-07-13 Issue: Volume:34 Page:1-11
ISSN:
Container-title:Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24
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Author:

Zhang Zihan¹^ORCID,Liu Richard¹^ORCID,Hanocka Rana¹^ORCID,Aberman Kfir²^ORCID

Affiliation:

1. University of Chicago, United States of America

2. Snap Inc., United States of America

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference52 articles.

1. Skeleton-aware networks for deep motion retargeting

2. Unpaired motion style transfer from video to animation

3. Kfir Aberman, Rundi Wu, Dani Lischinski, Baoquan Chen, and Daniel Cohen-Or. 2019. Learning Character-Agnostic Motion for Motion Retargeting in 2D. ACM Trans. Graph. 38, 4 (2019), 75.

4. Andreas Aristidou, Anastasios Yiannakidis, Kfir Aberman, Daniel Cohen-Or, Ariel Shamir, and Yiorgos Chrysanthou. 2021. Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure. arXiv preprint arXiv:2111.12159 (2021).

5. Rishabh Dabral Muhammad Hamza Mughal Vladislav Golyanik and Christian Theobalt. 2023. MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis. In Computer Vision and Pattern Recognition (CVPR).

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