$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

[해외논문] Deep Learning‐Based Unsupervised Human Facial Retargeting

Computer graphics forum : journal of the European Association for Computer Graphics, v.40 no.7, 2021년, pp.45 - 55  

Kim, Seonghyeon (KAIST, Visual Media Lab) ,  Jung, Sunjin (KAIST, Visual Media Lab) ,  Seo, Kwanggyoon (KAIST, Visual Media Lab) ,  i Ribera, Roger Blanco (C‐) ,  Noh, Junyong (JeS Gulliver Studios)

Abstract AI-Helper 아이콘AI-Helper

AbstractTraditional approaches to retarget existing facial blendshape animations to other characters rely heavily on manually paired data including corresponding anchors, expressions, or semantic parametrizations to preserve the characteristics of the original performance. In this paper, inspired by...

Keyword

참고문헌 (42)

  1. 10.1109/WACV.2018.00024 AnejaD. ChaudhuriB. ColburnA. FaiginG. ShapiroL. MonesB.: Learning to generate 3d stylized character expressions from humans. In2018 IEEE Winter Conference on Applications of Computer Vision (WACV)(2018) IEEE pp.160-169. 

  2. 10.1007/978-3-319-54184-6_9 AnejaD. ColburnA. FaiginG. ShapiroL. MonesB.: Modeling stylized character expressions via deep learning. InAsian conference on computer vision(2016) Springer pp.136-153. 

  3. 10.1109/CVPR.2018.00702 BaoJ. ChenD. WenF. LiH. HuaG.: Towards open-set identity preserving face synthesis. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2018) pp.6713-6722. 

  4. 10.1145/311535.311556 BlanzV. VetterT.: A morphable model for the synthesis of 3d faces. InProceedings of the 26th annual conference on Computer graphics and interactive techniques(1999) pp.187-194. 

  5. Cao, Chen, Agrawal, Vasu, De La Torre, Fernando, Chen, Lele, Saragih, Jason, Simon, Tomas, Sheikh, Yaser. Real-time 3D neural facial animation from binocular video. ACM transactions on graphics, vol.40, no.4, 1-17.

  6. Cao, Chen, Weng, Yanlin, Lin, Stephen, Zhou, Kun. 3D shape regression for real-time facial animation. ACM transactions on graphics, vol.32, no.4, 1-10.

  7. 10.1145/1111411.1111419 DengZ. ChiangP.-Y. FoxP. NeumannU.: Animating blendshape faces by cross-mapping motion capture data. InProceedings of the 2006 symposium on Interactive 3D graphics and games(2006) pp.43-48. 

  8. Palo Alto Friesen E. 5 3 2 1978 Facial action coding system: a technique for the measurement of facial movement 

  9. Gao, Ming, Pradhana, Andre, Han, Xuchen, Guo, Qi, Kot, Grant, Sifakis, Eftychios, Jiang, Chenfanfu. Animating fluid sediment mixture in particle-laden flows. ACM transactions on graphics, vol.37, no.4, 1-11.

  10. 10.1007/978-3-030-58529-7_10 GuoJ. ZhuX. YangY. YangF. LeiZ. LiS. Z.: Towards fast accurate and stable 3d dense face alignment.arXiv preprint arXiv:2009.09960(2020). 

  11. KingmaD. P. BaJ.: Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980(2014). 

  12. Kim, Hyeongwoo, Garrido, Pablo, Tewari, Ayush, Xu, Weipeng, Thies, Justus, Niessner, Matthias, Pérez, Patrick, Richardt, Christian, Zollhöfer, Michael, Theobalt, Christian. Deep video portraits. ACM transactions on graphics, vol.37, no.4, 1-14.

  13. Krizhevsky, Alex, Sutskever, Ilya, Hinton, Geoffrey E.. ImageNet classification with deep convolutional neural networks. Communications of the ACM, vol.60, no.6, 84-90.

  14. LewisJ. P. AnjyoK. RheeT. ZhangM. PighinF. DengZ.: Practice and theory of blendshape facial models. InEurographics(2014). 

  15. LiuM.-Y. BreuelT. KautzJ.: Unsupervised image-to-image translation networks. InAdvances in neural information processing systems(2017) pp.700-708. 

  16. 10.1145/3099564.3099581 LaineS. KarrasT. AilaT. HervaA. SaitoS. YuR. LiH. LehtinenJ.: Production-level facial performance capture using deep convolutional neural networks. InProceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation(2017) pp.1-10. 

  17. 10.1111/cgf.14062 NaruniecJ. HelmingerL. SchroersC. WeberR.: High-resolution neural face swapping for visual effects. InComputer Graphics Forum(2020) vol. 39 Wiley Online Library pp.173-184. 

  18. 10.1109/ICCV.2019.00728 NirkinY. KellerY. HassnerT.: Fsgan: Subject agnostic face swapping and reenactment. InProceedings of the IEEE/CVF International Conference on Computer Vision(2019) pp.7184-7193. 

  19. 10.1145/383259.383290 NohJ.-y. NeumannU.: Expression cloning. InProceedings of the 28th annual conference on Computer graphics and interactive techniques(2001) pp.277-288. 

  20. 10.1007/978-3-030-20876-9_8 NatsumeR. YatagawaT. MorishimaS.: Fsnet: An identity-aware generative model for image-based face swapping. InAsian Conference on Computer Vision(2018) Springer pp.117-132. 

  21. 10.1145/3230744.3230818 NatsumeR. YatagawaT. MorishimaS.: Rsgan: face swapping and editing using face and hair representation in latent spaces.arXiv preprint arXiv:1804.03447(2018). 

  22. PetrovI. GaoD. ChervoniyN. LiuK. MarangondaS. UméC. JiangJ. RPL. ZhangS. WuP. et al.: Deepfacelab: A simple flexible and extensible face swapping framework.arXiv preprint arXiv:2005.05535(2020). 

  23. Phong, Bui Tuong. Illumination for computer generated pictures. Communications of the ACM, vol.18, no.6, 311-317.

  24. RaviN. ReizensteinJ. NovotnyD. GordonT. LoW.-Y. JohnsonJ. GkioxariG.: Accelerating 3d deep learning with pytorch3d.arXiv:2007.08501(2020). 

  25. Ribera, Roger Blanco i, Zell, Eduard, Lewis, J. P., Noh, Junyong, Botsch, Mario. Facial retargeting with automatic range of motion alignment. ACM transactions on graphics, vol.36, no.4, 1-12.

  26. Song, Jaewon, Choi, Byungkuk, Seol, Yeongho, Noh, Junyong. Characteristic facial retargeting. Computer Animation and Virtual Worlds, vol.22, no.2, 187-194.

  27. 10.1145/2614106.2614108 SeolY. LewisJ. P.: Tuning facial animation in a mocap pipeline. InACM SIGGRAPH 2014 Talks.2014 pp.1-1. 

  28. Seol, Yeongho, Lewis, J.P., Seo, Jaewoo, Choi, Byungkuk, Anjyo, Ken, Noh, Junyong. Spacetime expression cloning for blendshapes. ACM transactions on graphics, vol.31, no.2, 1-12.

  29. Advances in Neural Information Processing Systems Siarohin A. 7137 32 2019 First order motion model for image animation 

  30. 10.1145/2947688.2947693 SeolY. MaW.-C. LewisJ.: Creating an actor-specific facial rig from performance capture. InProceedings of the 2016 Symposium on Digital Production(2016) pp.13-17. 

  31. Sumner, Robert W., Popović, Jovan. Deformation transfer for triangle meshes. ACM transactions on graphics, vol.23, no.3, 399-405.

  32. 10.1109/CVPR46437.2021.01344 SiarohinA. WoodfordO. RenJ. ChaiM. TulyakovS.: Motion representations for articulated animation. InCVPR(2021). 

  33. SimonyanK. ZissermanA.: Very deep convolutional networks for large-scale image recognition.arXiv preprint arXiv:1409.1556(2014). 

  34. 10.1109/CVPR.2019.01107 TewariA. BernardF. GarridoP. BharajG. ElgharibM. SeidelH.-P. PérezP. ZollhoferM. TheobaltC.: Fml: Face model learning from videos. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2019) pp.10812-10822. 

  35. 10.1109/CVPR.2019.00122 TranL. LiuF. LiuX.: Towards high-fidelity nonlinear 3d face morphable model. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2019) pp.1126-1135. 

  36. Tolosana, Ruben, Vera-Rodriguez, Ruben, Fierrez, Julian, Morales, Aythami, Ortega-Garcia, Javier. Deepfakes and beyond: A Survey of face manipulation and fake detection. Information fusion, vol.64, 131-148.

  37. Tewari, Ayush, Zollhöfer, Michael, Bernard, Florian, Garrido, Pablo, Kim, Hyeongwoo, Pérez, Patrick, Theobalt, Christian. High-Fidelity Monocular Face Reconstruction Based on an Unsupervised Model-Based Face Autoencoder. IEEE transactions on pattern analysis and machine intelligence, vol.42, no.2, 357-370.

  38. Thies, Justus, Zollhöfer, Michael, Nießner, Matthias, Valgaerts, Levi, Stamminger, Marc, Theobalt, Christian. Real-time expression transfer for facial reenactment. ACM transactions on graphics, vol.34, no.6, 1-14.

  39. Thies, Justus, Zollhöfer, Michael, Nießner, Matthias. Deferred neural rendering : image synthesis using neural textures. ACM transactions on graphics, vol.38, no.4, 1-12.

  40. 10.1109/CVPR.2016.262 ThiesJ. ZollhoferM. StammingerM. TheobaltC. NiessnerM.: Face2face: Real-time face capture and reenactment of rgb videos. InProceedings of the IEEE conference on computer vision and pattern recognition(2016) pp.2387-2395. 

  41. ZhangJ. ChenK. ZhengJ.: Facial expression retargeting from human to avatar made easy.IEEE Transactions on Visualization and Computer Graphics(2020). 

  42. 10.1109/CVPR.2018.00068 ZhangR. IsolaP. EfrosA. A. ShechtmanE. WangO.: The unreasonable effectiveness of deep features as a perceptual metric. InProceedings of the IEEE conference on computer vision and pattern recognition(2018) pp.586-595. 

LOADING...

활용도 분석정보

상세보기
다운로드
내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

관련 콘텐츠

유발과제정보 저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로