Seeing the Sound: Multilingual Lip Sync for Real-Time Face-to-Face Translation
Imagine a future where language is no longer a barrier to real-time conversations, enabling instant and lifelike communication across the globe. As cultural boundaries blur, the demand for seamless multilingual communication has become a critical technological challenge. This paper addresses the lac...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/14/1/7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588782398865408 |
|---|---|
| author | Amirkia Rafiei Oskooei Mehmet S. Aktaş Mustafa Keleş |
| author_facet | Amirkia Rafiei Oskooei Mehmet S. Aktaş Mustafa Keleş |
| author_sort | Amirkia Rafiei Oskooei |
| collection | DOAJ |
| description | Imagine a future where language is no longer a barrier to real-time conversations, enabling instant and lifelike communication across the globe. As cultural boundaries blur, the demand for seamless multilingual communication has become a critical technological challenge. This paper addresses the lack of robust solutions for real-time face-to-face translation, particularly for low-resource languages, by introducing a comprehensive framework that not only translates language but also replicates voice nuances and synchronized facial expressions. Our research tackles the primary challenge of achieving accurate lip synchronization across culturally diverse languages, filling a significant gap in the literature by evaluating the generalizability of lip sync models beyond English. Specifically, we develop a novel evaluation framework combining quantitative lip sync error metrics and qualitative assessments by human observers. This framework is applied to assess two state-of-the-art lip sync models with different architectures for Turkish, Persian, and Arabic languages, using a newly collected dataset. Based on these findings, we propose and implement a modular system that integrates language-agnostic lip sync models with neural networks to deliver a fully functional face-to-face translation experience. Inference Time Analysis shows this system achieves highly realistic, face-translated talking heads in real time, with a throughput as low as 0.381 s. This transformative framework is primed for deployment in immersive environments such as VR/AR, Metaverse ecosystems, and advanced video conferencing platforms. It offers substantial benefits to developers and businesses aiming to build next-generation multilingual communication systems for diverse applications. While this work focuses on three languages, its modular design allows scalability to additional languages. However, further testing in broader linguistic and cultural contexts is required to confirm its universal applicability, paving the way for a more interconnected and inclusive world where language ceases to hinder human connection. |
| format | Article |
| id | doaj-art-8adbf3447e3846e28c1d86140d9106a1 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-8adbf3447e3846e28c1d86140d9106a12025-01-24T13:27:51ZengMDPI AGComputers2073-431X2024-12-01141710.3390/computers14010007Seeing the Sound: Multilingual Lip Sync for Real-Time Face-to-Face TranslationAmirkia Rafiei Oskooei0Mehmet S. Aktaş1Mustafa Keleş2Computer Engineering Department, Yildiz Technical University, Istanbul 34320, TurkeyComputer Engineering Department, Yildiz Technical University, Istanbul 34320, TurkeyResearch and Development Center, Aktif Bank, Istanbul 34394, TurkeyImagine a future where language is no longer a barrier to real-time conversations, enabling instant and lifelike communication across the globe. As cultural boundaries blur, the demand for seamless multilingual communication has become a critical technological challenge. This paper addresses the lack of robust solutions for real-time face-to-face translation, particularly for low-resource languages, by introducing a comprehensive framework that not only translates language but also replicates voice nuances and synchronized facial expressions. Our research tackles the primary challenge of achieving accurate lip synchronization across culturally diverse languages, filling a significant gap in the literature by evaluating the generalizability of lip sync models beyond English. Specifically, we develop a novel evaluation framework combining quantitative lip sync error metrics and qualitative assessments by human observers. This framework is applied to assess two state-of-the-art lip sync models with different architectures for Turkish, Persian, and Arabic languages, using a newly collected dataset. Based on these findings, we propose and implement a modular system that integrates language-agnostic lip sync models with neural networks to deliver a fully functional face-to-face translation experience. Inference Time Analysis shows this system achieves highly realistic, face-translated talking heads in real time, with a throughput as low as 0.381 s. This transformative framework is primed for deployment in immersive environments such as VR/AR, Metaverse ecosystems, and advanced video conferencing platforms. It offers substantial benefits to developers and businesses aiming to build next-generation multilingual communication systems for diverse applications. While this work focuses on three languages, its modular design allows scalability to additional languages. However, further testing in broader linguistic and cultural contexts is required to confirm its universal applicability, paving the way for a more interconnected and inclusive world where language ceases to hinder human connection.https://www.mdpi.com/2073-431X/14/1/7talking head generationlip synchronizationface-to-face translationcomputer visiondeep learninggenerative AI |
| spellingShingle | Amirkia Rafiei Oskooei Mehmet S. Aktaş Mustafa Keleş Seeing the Sound: Multilingual Lip Sync for Real-Time Face-to-Face Translation Computers talking head generation lip synchronization face-to-face translation computer vision deep learning generative AI |
| title | Seeing the Sound: Multilingual Lip Sync for Real-Time Face-to-Face Translation |
| title_full | Seeing the Sound: Multilingual Lip Sync for Real-Time Face-to-Face Translation |
| title_fullStr | Seeing the Sound: Multilingual Lip Sync for Real-Time Face-to-Face Translation |
| title_full_unstemmed | Seeing the Sound: Multilingual Lip Sync for Real-Time Face-to-Face Translation |
| title_short | Seeing the Sound: Multilingual Lip Sync for Real-Time Face-to-Face Translation |
| title_sort | seeing the sound multilingual lip sync for real time face to face translation |
| topic | talking head generation lip synchronization face-to-face translation computer vision deep learning generative AI |
| url | https://www.mdpi.com/2073-431X/14/1/7 |
| work_keys_str_mv | AT amirkiarafieioskooei seeingthesoundmultilinguallipsyncforrealtimefacetofacetranslation AT mehmetsaktas seeingthesoundmultilinguallipsyncforrealtimefacetofacetranslation AT mustafakeles seeingthesoundmultilinguallipsyncforrealtimefacetofacetranslation |