|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM378608762 |
| 003 |
DE-627 |
| 005 |
20241009232414.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
241008s2024 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TVCG.2024.3476275
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1562.xml
|
| 035 |
|
|
|a (DE-627)NLM378608762
|
| 035 |
|
|
|a (NLM)39374282
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Zhuang, Yixiang
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Learn2Talk
|b 3D Talking Face Learns from 2D Talking Face
|
| 264 |
|
1 |
|c 2024
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 09.10.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status Publisher
|
| 520 |
|
|
|a The speech-driven facial animation technology is generally categorized into two main types: 3D and 2D talking face. Both of these have garnered considerable research attention in recent years. However, to our knowledge, the research into 3D talking face has not progressed as deeply as that of 2D talking face, particularly in terms of lip-sync and perceptual mouth movements. The lip-sync necessitates an impeccable synchronization between mouth motion and speech audio. The speech perception derived from the perceptual mouth movements should resemble that of the driving audio. To mind the gap between the two sub-fields, we propose Learn2Talk, a learning framework that enhances 3D talking face network by integrating two key insights from the field of 2D talking face. Firstly, drawing inspiration from the audio-video sync network, we develop a 3D sync-lip expert model for the pursuit of lip-sync between audio and 3D facial motions. Secondly, we utilize a teacher model, carefully chosen from among 2D talking face methods, to guide the training of the audio-to-3D motions regression network, thereby increasing the accuracy of 3D vertex movements. Extensive experiments demonstrate the superiority of our proposed framework over state-of-the-art methods in terms of lip-sync, vertex accuracy and perceptual movements. Finally, we showcase two applications of our framework: audio-visual speech recognition and speech-driven 3D Gaussian Splatting-based avatar animation. The project page of this paper is: https://lkjkjoiuiu.github.io/Learn2Talk/
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Cheng, Baoping
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Cheng, Yao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Jin, Yuntao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Renshuai
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Li, Chengyang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Cheng, Xuan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liao, Jing
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Lin, Juncong
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g PP(2024) vom: 07. Okt.
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnns
|
| 773 |
1 |
8 |
|g volume:PP
|g year:2024
|g day:07
|g month:10
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TVCG.2024.3476275
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d PP
|j 2024
|b 07
|c 10
|