RealTalk: Real-time and Realistic Audio-driven Face Generation with 3D
  Facial Prior-guided Identity Alignment Network
Person-generic audio-driven face generation is a challenging task in computer
vision. Previous methods have achieved remarkable progress in audio-visual
synchronization, but there is still a significant gap between current results
and practical applications. The challenges are two-fold: 1) Preserving unique
individual traits for achieving high-precision lip synchronization. 2)
Generating high-quality facial renderings in real-time performance. In this
paper, we propose a novel generalized audio-driven framework RealTalk, which
consists of an audio-to-expression transformer and a high-fidelity
expression-to-face renderer. In the first component, we consider both identity
and intra-personal variation features related to speaking lip movements. By
incorporating cross-modal attention on the enriched facial priors, we can
effectively align lip movements with audio, thus attaining greater precision in
expression prediction. In the second component, we design a lightweight facial
identity alignment (FIA) module which includes a lip-shape control structure
and a face texture reference structure. This novel design allows us to generate
fine details in real-time, without depending on sophisticated and inefficient
feature alignment modules. Our experimental results, both quantitative and
qualitative, on public datasets demonstrate the clear advantages of our method
in terms of lip-speech synchronization and generation quality. Furthermore, our
method is efficient and requires fewer computational resources, making it
well-suited to meet the needs of practical applications.
6684235c02026ea33b4052dc
Xiaozhong Ji
admin_assigned
2024-07-03T15:11:50.184Z
666a59bff0d87d9c3b1dd907
/avatars/4af5a47d78bca525c7ec985a390408a4.svg
Xiaozhong Ji
user
xiaozhongji
6684235c02026ea33b4052dd
Chuming Lin
admin_assigned
2024-07-03T15:11:56.391Z
6307176edc6b7663aa918217
/avatars/b84528fedc607886d3827e10bd23be7b.svg
chuminglin
user
chuminglin
6684235c02026ea33b4052de
Zhonggan Ding
6684235c02026ea33b4052df
Ying Tai
claimed_verified
2024-07-02T21:08:48.745Z
65734004769f3ee9bde1af10
/avatars/d6310ed861972fd691687d8f47413f33.svg
Ying Tai
user
yingtai
6684235c02026ea33b4052e0
Jian Yang
6684235c02026ea33b4052e1
Junwei Zhu
admin_assigned
2024-07-03T15:12:15.077Z
65aadb2b9aba49e1d03cfccc
/avatars/b7c48dc999bf2f94018315b546d50eeb.svg
Junwei Zhu
user
ktpt123
6684235c02026ea33b4052e2
Xiaobin Hu
6684235c02026ea33b4052e3
Jiangning Zhang
6684235c02026ea33b4052e4
Donghao Luo
6684235c02026ea33b4052e5
Chengjie Wang
admin_assigned
2024-07-03T15:13:00.328Z
65729087379284b904a6d81d
/avatars/0e7968b1d9221d523a54e3ac787e449d.svg
Chengjie Wang
user
chengjie-wang
RealTalk: Real-time and Realistic Audio-driven Face Generation with 3D
  Facial Prior-guided Identity Alignment Network
Person-generic audio-driven face generation is a challenging task in computer
vision. Previous methods have achieved remarkable progress in audio-visual
synchronization, but there is still a significant gap between current results
and practical applications. The challenges are two-fold: 1) Preserving unique
individual traits for achieving high-precision lip synchronization. 2)
Generating high-quality facial renderings in real-time performance. In this
paper, we propose a novel generalized audio-driven framework RealTalk, which
consists of an audio-to-expression transformer and a high-fidelity
expression-to-face renderer. In the first component, we consider both identity
and intra-personal variation features related to speaking lip movements. By
incorporating cross-modal attention on the enriched facial priors, we can
effectively align lip movements with audio, thus attaining greater precision in
expression prediction. In the second component, we design a lightweight facial
identity alignment (FIA) module which includes a lip-shape control structure
and a face texture reference structure. This novel design allows us to generate
fine details in real-time, without depending on sophisticated and inefficient
feature alignment modules. Our experimental results, both quantitative and
qualitative, on public datasets demonstrate the clear advantages of our method
in terms of lip-speech synchronization and generation quality. Furthermore, our
method is efficient and requires fewer computational resources, making it
well-suited to meet the needs of practical applications.
A new audio-driven face generation framework RealTalk uses an audio-to-expression transformer and a high-fidelity expression-to-face renderer for precise lip synchronization and high-quality real-time facial rendering.
audio-driven framework audio-to-expression transformer high-fidelity expression-to-face renderer cross-modal attention facial priors lip-shape control structure face texture reference structure lip-speech synchronization