Disentangling audio content and emotion with adaptive instance normalization for expressive facial animation synthesis-Reference-Cited by-同舟云学术

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Disentangling audio content and emotion with adaptive instance normalization for expressive facial animation synthesis

Published:2022-06 Issue:3-4 Volume:33 Page:
ISSN:1546-4261
Container-title:Computer Animation and Virtual Worlds
language:en
Short-container-title:Computer Animation & Virtual

Author:

Chang Che‐Jui¹^ORCID,Zhao Long¹^ORCID,Zhang Sen¹,Kapadia Mubbasir¹

Affiliation:

1. Department of Computer Science Rutgers University Piscataway New Jersey USA

Funder

National Science Foundation

U.S. Department of Homeland Security

Publisher

Wiley

Subject

Computer Graphics and Computer-Aided Design,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cav.2076

Reference31 articles.

1. A deep learning approach for generalized speech animation

2. Audio-driven facial animation by joint end-to-end learning of pose and emotion

3. CudeiroD BolkartT LaidlawC RanjanA BlackMJ.Capture learning and synthesis of 3D speaking styles. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2019. p. 10101–11.

4. HuangX BelongieS.Arbitrary style transfer in real‐time with adaptive instance normalization. Proceedings of the IEEE International Conference on Computer Vision; 2017. p. 1501–10.

5. WangK QianyiW SongL YangZ WayneW QianC et alMead: a large‐scale audio‐visual dataset for emotional talking‐face generation. ECCV; 2020.

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