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刘菲， 张堃博， 杨青， 周树波， 王云龙， 孙哲南. 2024. 三维人脸成像及重建技术综述. 中国图象图形学报， 29(09):2441-2470
Liu Fei， Zhang Kunbo， Yang Qing， Zhou Shubo， Wang Yunlong， Sun Zhenan. 2024. 3D face imaging and reconstruction technology： a review. Journal of Image and Graphics， 29(09):2441-2470
得益于新型三维视觉测量技术及深度学习模型的飞速发展，三维视觉成为人工智能、虚拟现实等领域的重要支撑技术，三维人脸成像及重建技术取得了突破性进展，不仅能够更好地应对光照、遮挡、表情和姿态等变化，同时增大了伪造攻击难度，大大推动了真实感“虚拟数字人”的重建与渲染，有效提升了人脸系统的安全性。本文对三维人脸成像技术和重建模型进行了全面综述，尤其对基于深度学习的三维人脸重建进行系统深入地分析。首先，对三维人脸成像设备及采集系统进行详细梳理及对比归纳，并介绍了基于新传感技术的人脸成像系统；然后，对基于深度学习的三维人脸重建模型进行系统分析，从输入数据源角度分为基于单目图像、基于多目图像、基于视频和基于语音的三维人脸重建算法4类。通过深入分析，总结三维人脸成像的研究现状及面临的难点与挑战，对未来发展方向及应用进行积极探讨与展望。本文涵盖了近5年经典的三维人脸成像及重建相关的技术与研究，为人脸研究、发展和应用提供了很好的参考。
As the breakthrough technology of artificial intelligence （AI） in the big data era， deep learning （DL） has prompted the renewed upsurge of face technology. Powered by rapid developments of new technologies， such as three-dimensional （3D） vision measurement， image processing chips， and DL models， 3D vision transformed into a key supporting technology in AI， visual reality， etc. The studies and applications of 3D facial imaging and reconstruction technologies have achieved important breakthroughs. 3D face data represent exact multidimensional facial attributes on account of rich visual information， such as texture， shape， space， etc. Moreover， 3D face data shows robust changes in large occlusions， expressions， and poses and increases the difficulty of forgery attack. Therefore， 3D face imaging and reconstruction effectively promote realistic “virtual digital human” reconstruction and rendering. In addition， these processes contribute to the improved security of the face system. In this paper， we comprehensively study the 3D face imaging technology and reconstruction models. The 3D face reconstruction methods based on DL are systematically and deeply analyzed. First， the development and innovation of 3D face imaging devices and capturing systems are discussed through a summary of public 3D face datasets. The devices and systems include consumer imaging devices （such as Kinect） and complex hybrid systems that fuse active and passive 3D imaging technologies to achieve precise geometry and appearance. Moreover， 3D face imaging based on new sensing technologies are introduced. Then， from the perspective of input resources， 3D face reconstruction methods based on DL are categorized into monocular， multiview， video and audio reconstruction methods. 3D face imaging technology introduces public classic 3D face datasets， popular 3D face imaging devices， and capturing systems. Most high-quality 3D face datasets， such as BU-3DFE， FaceScape and FaceVerse， are captured through a large imaging volume with a certain number of high-resolution cameras and controlled lighting conditions. They play key roles in applications of realistic rendering， driven animation， retargeting， etc. On the other hand， novel optical devices and imaging modules with small size and lightweight algorithm must be innovated for tiny AI as intelligent mobile devices. For 3D face reconstruction based on DL， monocular reconstruction has become the most popular technology. The state-of-the-art 3D face reconstruction method is generally self-supervised training on large-scale 2D face databases. The difficulties encountered in 3D face reconstruction include the lack of large-scale 3D face datasets， occlusions and poses of in-the-wild 2D face images， continuous expression deformations， etc. The DL network structure is categorized into general deep convolutional neural network （such as ResNet， U-Net， and Autoencoder）， generative adversarial networks （GANs）， implicit neural representation （INR） （such as neural radiance field （NeRF） and signed distance functions （SDF））， and Transformer. 3DMM and FLAME are widely used 3D face representation models. The StyleGAN model gives excellent performance in recovering high-quality face texture. INR has achieved remarkable results in 3D scene reconstruction， and the NeRF model plays an important role in the reconstruction of accurate head avatars. The combination of NeRF with GAN shows great potential in the reconstruction of high-fidelity 3D face geometry and realistic rendering appearances. Moreover， the Transformer model， which greatly improves the breakthrough of accuracy and speed， is mainly used in audio-driven 3D face reconstruction. Through in-depth analyses， the research difficulties accompanying 3D face are summarized， and future developments are actively being discussed and explored. Although recent research has made amazing progresses， challenges on how to improve the robustness and generalization to real-world lighting， extreme expressions/poses， and how to effectively disentangle facial attributes （such as identity， expression， albedo， and specular reflectance） and recover accurate detailed geometry of facial motions （such as wrinkles）. In this study， we proposed a comprehensive and systematic review and covered classical technologies and studies on 3D face imaging and reconstruction in the last five years to provide a good reference for face studies， developments， and applications.
三维人脸成像三维人脸重建深度学习 （DL）生成对抗网络 （GAN）隐式神经表示 （INR）
3D face imaging3D face reconstructiondeep learning （DL）generative adversarial network （GAN）implicit neural representation （INR）
3dMD Inc. ［EB/OL］. ［2024-06-06］. https://3dmd.com/https://3dmd.com/
Abrevaya V F， BoukhaymaA， Torr P H S and BoyerE. 2020. Cross-modal deep face normals with deactivable skip connections//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 4978-4988 ［DOI： 10.1109/CVPR42600.2020.00503http://dx.doi.org/10.1109/CVPR42600.2020.00503］
Azinović D， Maury O， Hery C， Nießner M and Thies J. 2023. High-res facial appearance capture from polarized smartphone images//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 16836-16846 ［DOI： 10.1109/CVPR52729.2023.01615http://dx.doi.org/10.1109/CVPR52729.2023.01615］
Bae S I， Lee S， Kwon J M， Kim H K， Jang K W， Lee D and Jeong K H. 2022. Machine-learned light-field camera that reads facial expression from high-contrast and illumination invariant 3D facial images. Advanced Intelligent Systems， 4（4）： #2100182 ［DOI： 10.1002/aisy.202100182http://dx.doi.org/10.1002/aisy.202100182］
Bagdanov A D， Del Bimbo A and Masi I. 2011. The florence 2D/3D hybrid face dataset//Proceedings of 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding. Scottsdale， USA： ACM： 79-80 ［DOI： 10.1145/2072572.2072597http://dx.doi.org/10.1145/2072572.2072597］
Bai H R， Kang D， Zhang H X， Pan J S and Bao L C. 2023. FFHQ-UV： normalized facial UV-texture dataset for 3D face reconstruction//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 362-371 ［DOI： 10.1109/CVPR52729.2023.00043http://dx.doi.org/10.1109/CVPR52729.2023.00043］
Bai Z Q， Cui Z P， Rahim J A， Liu X M and Tan P. 2020. Deep facial non-rigid multi-view stereo//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 5849-5859 ［DOI： 10.1109/cvpr42600.2020.00589http://dx.doi.org/10.1109/cvpr42600.2020.00589］
Bansal A， Nanduri A， Castillo C D， Ranjan R and Chellappa R. 2017. UMDFaces： an annotated face dataset for training deep networks//Proceedings of 2017 IEEE International Joint Conference on Biometrics. Denver， USA： IEEE： 464-473 ［DOI： 10.1109/BTAS.2017.8272731http://dx.doi.org/10.1109/BTAS.2017.8272731］
Bao L C， Lin X K， Chen Y J， Zhang H X， Wang S， Zhe X F， Kang D， Huang H Z， Jiang X W， Wang J， Yu D and Zhang Z Y. 2021. High-fidelity 3D digital human head creation from RGB-D selfies. ACM Transactions on Graphics， 41（1）： #3 ［DOI： 10.1145/3472954http://dx.doi.org/10.1145/3472954］
Beeler T， Bickel B， Beardsley P， Sumner B and Gross M. 2010. High-quality single-shot capture of facial geometry. ACM Transactions on Graphics， 29（4）： #40 ［DOI： 10.1145/1778765.1778777http://dx.doi.org/10.1145/1778765.1778777］
Beeler T， Hahn F， Bradley D， Bickel B， Beardsley P， Gotsman C， Sumner R W and Gross M. 2011. High-quality passive facial performance capture using anchor frames. ACM Transactions on Graphics， 30（4）： #75 ［DOI： 10.1145/2010324.1964970http://dx.doi.org/10.1145/2010324.1964970］
Benitez-Quiroz C F， Srinivasan R and Martinez A M. 2016. EmotioNet： an accurate， real-time algorithm for the automatic annotation of a million facial expressions in the wild//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 5562-5570 ［DOI： 10.1109/CVPR.2016.600http://dx.doi.org/10.1109/CVPR.2016.600］
Blanz V and Vetter T. 1999. A morphable model for the synthesis of 3D faces//Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. ［s.l.］： ACM Press/Addison-Wesley Publishing Co.： 187-194 ［DOI： 10.1145/311535.311556http://dx.doi.org/10.1145/311535.311556］
Booth J， Roussos A， Zafeiriou S， Ponniah A and Dunaway D. 2016. A 3D morphable model learnt from 10 000 faces//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 5543-5552 ［DOI： 10.1109/CVPR.2016.598http://dx.doi.org/10.1109/CVPR.2016.598］
Bouritsas G， Bokhnyak S， Ploumpis S， Zafeiriou S and Bronstein M. 2019. Neural 3D morphable models： spiral convolutional networks for 3D shape representation learning and generation//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 7212-7221 ［DOI： 10.1109/ICCV.2019.00731http://dx.doi.org/10.1109/ICCV.2019.00731］
Brock A， Donahue J and Simonyan K. 2019. Large scale GAN training for high fidelity natural image synthesis ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/1809.11096.pdfhttps://arxiv.org/pdf/1809.11096.pdf
Bulat A and Tzimiropoulos G. 2017. How far are we from solving the 2D and 3D face alignment problem？ （and a dataset of 230 000 3D facial landmarks）//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 1021-1030 ［DOI： 10.1109/ICCV.2017.116http://dx.doi.org/10.1109/ICCV.2017.116］
Cao C， Simon T， Kim J K， Schwartz G， Zollhoefer M， Saito S S， Lombardi S， Wei S E， Belko D， Yu S I， Sheikh Y and Saragih J. 2022. Authentic volumetric avatars from a phone scan. ACM Transactions on Graphics， 41（4）： #163 ［DOI： 10.1145/3528223.3530143http://dx.doi.org/10.1145/3528223.3530143］
Cao C， Weng Y L， Zhou S， Tong Y Y and Zhou K. 2014. FaceWarehouse： a 3D facial expression database for visual computing. IEEE Transactions on Visualization and Computer Graphics， 20（3）： 413-425 ［DOI： 10.1109/TVCG.2013.249http://dx.doi.org/10.1109/TVCG.2013.249］
Cao Q， Shen L， Xie W D， Parkhi O M and Zisserman A. 2018. VGGFace2： a dataset for recognising faces across pose and age//Proceedings of the 13th IEEE International Conference on Automatic Face and Gesture Recognition. Xi’an， China： ACM： 67-74 ［DOI： 10.1109/FG.2018.00020http://dx.doi.org/10.1109/FG.2018.00020］
Chai Z H， Zhang H X， Ren J， Kang D， Xu Z Z， Zhe X F， Yuan C and Bao L C. 2022. REALY： rethinking the evaluation of 3D face reconstruction//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 74-92 ［DOI： 10.1007/978-3-031-20074-8_5http://dx.doi.org/10.1007/978-3-031-20074-8_5］
Chan E R， Lin C Z， Chan M A， Nagano K， Pan B X， de Mello S， Gallo O， Guibas L， Tremblay J， Khamis S， Karras T and Wetzstein G. 2022. Efficient geometry-aware 3D generative adversarial networks//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 16102-16112 ［DOI： 10.1109/CVPR52688.2022.01565http://dx.doi.org/10.1109/CVPR52688.2022.01565］
Chatziagapi A and Samaras D. 2023. AVFace： towards detailed audio-visual 4D face reconstruction//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 16878-16889 ［DOI： 10.1109/CVPR52729.2023.01619http://dx.doi.org/10.1109/CVPR52729.2023.01619］
Chen B C， Chen C S and Hsu W H. 2014. Cross-age reference coding for age-invariant face recognition and retrieval//Proceedings of the 13th European Conference on Computer Vision. Zurich， Switzerland： Springer： 768-783 ［DOI： 10.1007/978-3-319-10599-4_49http://dx.doi.org/10.1007/978-3-319-10599-4_49］
Chen G Y， Han K and Wong K Y K. 2018. PS-FCN： a flexible learning framework for photometric stereo//Proceedings of the 15th European Conference on Computer Vision. Munich， Germany： Springer： 3-19 ［DOI： 10.1007/978-3-030-01240-3_1http://dx.doi.org/10.1007/978-3-030-01240-3_1］
Cheng S Y， Kotsia I， Pantic M and Zafeiriou S. 2018. 4DFAB： a large scale 4D database for facial expression analysis and biometric applications//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 5117-5126 ［DOI： 10.1109/CVPR.2018.00537http://dx.doi.org/10.1109/CVPR.2018.00537］
Chrysos G G， Antonakos E， Zafeiriou S and Snape P. 2015. Offline deformable face tracking in arbitrary videos//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop. Santiago， Chile： IEEE： 954-962 ［DOI： 10.1109/ICCVW.2015.126http://dx.doi.org/10.1109/ICCVW.2015.126］
Chung J S， Nagrani A and Zisserman A. 2018. VoxCeleb2： deep speaker recognition//Interspeech 2018. Hyderabad， India： ［s.n.］： 1086-1090 ［DOI： 10.21437/Interspeech.2018-1929http://dx.doi.org/10.21437/Interspeech.2018-1929］
Cudeiro D， Bolkart T， Laidlaw C， Ranjan A and Black M J. 2019. Capture， learning， and synthesis of 3D speaking styles//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 10093-10103 ［DOI： 10.1109/CVPR.2019.01034http://dx.doi.org/10.1109/CVPR.2019.01034］
Dai H， Pears N， Smith W and Duncan C. 2017. A 3D morphable model of craniofacial shape and texture variation//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 3104-3112 ［DOI： 10.1109/ICCV.2017.335http://dx.doi.org/10.1109/ICCV.2017.335］
Dai H， Pears N， Smith W and Duncan C. 2020. Statistical modeling of craniofacial shape and texture. International Journal of Computer Vision， 128（2）： 547-571 ［DOI： 10.1007/s11263-019-01260-7http://dx.doi.org/10.1007/s11263-019-01260-7］
Daněček R， Black M and Bolkart T. 2022. EMOCA： emotion driven monocular face capture and animation//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 20279-20290 ［DOI： 10.1109/CVPR52688.2022.01967http://dx.doi.org/10.1109/CVPR52688.2022.01967］
Debevec P， Hawkins T， Tchou C， Duiker H P， Sarokin W and Sagar M. 2000. Acquiring the reflectance field of a human face//Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques. ［s.l.］： ACM Press/Addison-Wesley Publishing Co.： 145-156 ［DOI： 10.1145/344779.344855http://dx.doi.org/10.1145/344779.344855］
Debevec P. 2012. The light stages and their applications to photoreal digital actors. SIGGRAPH Asia， 2（4）： 1-4
Deng J K， Guo J， Liu T L， Gong M M and Zafeiriou S. 2020a. Sub-center ArcFace： boosting face recognition by large-scale noisy web faces//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 741-757 ［DOI： 10.1007/978-3-030-58621-8_43http://dx.doi.org/10.1007/978-3-030-58621-8_43］
Deng J K， Guo J， Ververas E， Kotsia I and Zafeiriou S. 2020b. RetinaFace： single-shot multi-level face localisation in the wild//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 5202-5211 ［DOI： 10.1109/CVPR42600.2020.00525http://dx.doi.org/10.1109/CVPR42600.2020.00525］
Deng J K， Guo J， Xue N N and Zafeiriou S. 2019a. ArcFace： additive angular margin loss for deep face recognition//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 4685-4694 ［DOI： 10.1109/CVPR.2019.00482http://dx.doi.org/10.1109/CVPR.2019.00482］
Deng Y， Yang J L， Xu S C， Chen D， Jia Y D and Tong X. 2019b. Accurate 3D face reconstruction with weakly-supervised learning： from single image to image set//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach， USA： IEEE： 285-295 ［DOI： 10.1109/CVPRW.2019.00038http://dx.doi.org/10.1109/CVPRW.2019.00038］
DI4D Inc. ［EB/OL］. ［2024-01-24］. https://di4d.com/https://di4d.com/
Dib A， Ahn J， Thébault C， Gosselin P H and Chevallier L. 2023. S2F2： self-supervised high fidelity face reconstruction from monocular image//Proceedings of the 17th IEEE International Conference on Automatic Face and Gesture Recognition. Waikoloa Beach， USA： ACM： 1-8 ［DOI： 10.1109/FG57933.2023.10042713http://dx.doi.org/10.1109/FG57933.2023.10042713］
Dib A， Thébault C， Ahn J， Gosselin P H， Theobalt C and Chevallier L. 2021. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 12799-12809 ［DOI： 10.1109/ICCV48922.2021.01258http://dx.doi.org/10.1109/ICCV48922.2021.01258］
Egger B， Smith W A P， Tewari A， Wuhrer S， Zollhoefer M， Beeler T， Bernard F， Bolkart T， Kortylewski A， Romdhani S， Theobalt C， Blanz V and Vetter T. 2020. 3D morphable face models-past， present， and future. ACM Transactions on Graphics， 39（5）： #157 ［DOI： 10.1145/3395208http://dx.doi.org/10.1145/3395208］
Fan Y R， Lin Z J， Saito J， Wang W P and Komura T. 2022. FaceFormer： speech-driven 3D facial animation with transformers//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 18749-18758 ［DOI： 10.1109/CVPR52688.2022.01821http://dx.doi.org/10.1109/CVPR52688.2022.01821］
Feng Y， Feng H W， Black M J and Bolkart T. 2021. Learning an animatable detailed 3D face model from in-the-wild images. ACM Transactions on Graphics， 40（4）： #88 ［DOI： 10.1145/3450626.3459936http://dx.doi.org/10.1145/3450626.3459936］
Feng Y， Wu F， Shao X H， Wang Y F and Zhou X. 2018a. Joint 3D face reconstruction and dense alignment with position map regression network//Proceedings of the 15th European Conference on Computer Vision. Munich， Germany： Springer： 557-574 ［DOI： 10.1007/978-3-030-01264-9_33http://dx.doi.org/10.1007/978-3-030-01264-9_33］
Feng Z H， Huber P， Kittler J， Hancock P， Wu X J， Zhao Q J， Koppen P and Raetsch M. 2018b. Evaluation of dense 3D reconstruction from 2D face images in the wild//Proceedings of the 13th IEEE International Conference on Automatic Face and Gesture Recognition. Xi’an， China： IEEE： 780-786 ［DOI： 10.1109/FG.2018.00123http://dx.doi.org/10.1109/FG.2018.00123］
Fu H B， Bian S J， Chaudhry E， Iglesias A， You L H and Zhang J J. 2021. State-of-the-Art in 3D face reconstruction from a single RGB image//Proceedings of the 21st International Conference on Computational Science. Krakow， Poland： Springer： 31-44 ［DOI： 10.1007/978-3-030-77977-1_3http://dx.doi.org/10.1007/978-3-030-77977-1_3］
Fyffe G， Jones A， Alexander O， Ichikari R and Debevec P. 2014. Driving high-resolution facial scans with video performance capture. ACM Transactions on Graphics， 34（1）： #8 ［DOI： 10.1145/2638549http://dx.doi.org/10.1145/2638549］
Gafni G， Thies J， Zollhöfer M and Nießner M. 2021. Dynamic neural radiance fields for monocular 4D facial avatar reconstruction.//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 8645-8654 ［DOI： 10.1109/CVPR46437.2021.00854http://dx.doi.org/10.1109/CVPR46437.2021.00854］
Galanakis S， Gecer B， Lattas A and Zafeiriou S. 2023. 3DMM-RF： convolutional radiance fields for 3D face modeling//Proceedings of 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa， USA： IEEE： 3525-3536 ［DOI： 10.1109/WACV56688.2023.00353http://dx.doi.org/10.1109/WACV56688.2023.00353］
Gao X， Zhong C L， Xiang J， Hong Y， Guo Y D and Zhang J Y. 2022. Reconstructing personalized semantic facial NeRF models from monocular video. ACM Transactions on Graphics， 41（6）： #200 ［DOI： 10.1145/3550454.3555501http://dx.doi.org/10.1145/3550454.3555501］
Gecer B， Deng J K and Zafeiriou S. 2021. OSTeC： one-shot texture completion//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 7624-7634 ［DOI： 10.1109/CVPR46437.2021.00754http://dx.doi.org/10.1109/CVPR46437.2021.00754］
Gecer B， Lattas A， Ploumpis S， Deng J K， Papaioannou A， Moschoglou S and Zafeiriou S. 2020. Synthesizing coupled 3D face modalities by trunk-branch generative adversarial networks//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 415-433 ［DOI： 10.1007/978-3-030-58526-6_25http://dx.doi.org/10.1007/978-3-030-58526-6_25］
Gecer B， Ploumpis S， Kotsia I and Zafeiriou S. 2019. GANFIT： generative adversarial network fitting for high fidelity 3D face reconstruction//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 1155-1164 ［DOI： 10.1109/CVPR.2019.00125http://dx.doi.org/10.1109/CVPR.2019.00125］
Gerig T， Forster A M， Blumer C， Egger B， Luthi M， Schoenborn S and Vetter T. 2018. Morphable face models——an open framework//Proceedings of the 13th IEEE International Conference on Automatic Face and Gesture Recognition. Xi’an， China： ACM： 75-82 ［DOI：10.1109/FG.2018.00021http://dx.doi.org/10.1109/FG.2018.00021］
Ghosh A， Fyffe G， Tunwattanapong B， Busch J， Yu X M and Debevec P. 2011. Multiview face capture using polarized spherical gradient illumination. ACM Transactions on Graphics， 30（6）： 1-10 ［DOI： 10.1145/2070781.2024163http://dx.doi.org/10.1145/2070781.2024163］
Giebenhain S， Kirschstein T， Georgopoulos M， Rünz M， Agapito L and Nießner M. 2023. Learning neural parametric head models//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 21003-21012 ［DOI： 10.1109/CVPR52729.2023.02012http://dx.doi.org/10.1109/CVPR52729.2023.02012］
Gotardo P， Riviere J， Bradley D， Ghosh A and Beeler T. 2018. Practical dynamic facial appearance modeling and acquisition. ACM Transactions on Graphics， 37（6）： #232 ［DOI： 10.1145/3272127.3275073http://dx.doi.org/10.1145/3272127.3275073］
Grassal P W， Prinzler M， Leistner T， Rother C， Nießner M and Thies J. 2022. Neural head avatars from monocular RGB videos//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 18632-18643 ［DOI： 10.1109/CVPR52688.2022.01810http://dx.doi.org/10.1109/CVPR52688.2022.01810］
Gross R， Matthews I， Cohn J， Kanade T and Baker S. 2010. Multi-PIE. Image and Vision Computing， 28（5）： 807-813 ［DOI： 10.1016/j.imavis.2009.08.002http://dx.doi.org/10.1016/j.imavis.2009.08.002］
Gu J T， Liu L J， Wang P and Theobalt C. 2021. StyleNeRF： a style-based 3D-aware generator for high-resolution image synthesis ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/ 2110.08985.pdfhttps://arxiv.org/pdf/2110.08985.pdf
Guo J， Yu J K， Lattas A and Deng J K. 2023a. Perspective reconstruction of human faces by joint mesh and landmark regression//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 350-365 ［DOI： 10.1007/978-3-031-25072-9_23http://dx.doi.org/10.1007/978-3-031-25072-9_23］
Guo J Z， Zhu X Y， Yang Y， Yang F， Lei Z and Li S Z. 2020. Towards fast， accurate and stable 3D dense face alignment//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 152-168 ［DOI： 10.1007/978-3-030-58529-7_10http://dx.doi.org/10.1007/978-3-030-58529-7_10］
Guo L W， Zhu H， Lu Y X， Wu M H and Cao X. 2023b. RAFaRe： learning robust and accurate non-parametric 3D face reconstruction from pseudo 2D and 3D pairs//Proceedings of the 37th AAAI Conference on Artificial Intelligence. Washington， USA： AAAI： 719-727 ［DOI： 10.1609/aaai.v37i1.25149http://dx.doi.org/10.1609/aaai.v37i1.25149］
Han Y X， Wang Z B and Xu F. 2023. Learning a 3D morphable face reflectance model from low-cost data//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 8598-8608 ［DOI： 10.1109/CVPR52729.2023.00831http://dx.doi.org/10.1109/CVPR52729.2023.00831］
Hannun A， Case C， Casper J， Catanzaro B， Diamos G， Elsen E， Prenger R， Satheesh S， Sengupta S， Coates A and Ng A Y. 2014. Deep speech： scaling up end-to-end speech recognition ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/1412.5567.pdfhttps://arxiv.org/pdf/1412.5567.pdf
He J Y， Huang H B， Zhang H Y， Sun M Y， Liu Y H and Zhou Z H. 2022. Review of 3D face reconstruction based on single image. Computer Science， 49（2）： 40-50
何嘉玉， 黄宏博， 张红艳， 孙牧野， 刘亚辉， 周哲海. 2022. 基于深度学习的单幅图像三维人脸重建研究综述. 计算机科学， 49（2）： 40-50 ［DOI： 10.11896/jsjkx.210500215http://dx.doi.org/10.11896/jsjkx.210500215］
He K M， Zhang X Y， Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 770-778 ［DOI： 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90］
Hong Y， Peng B， Xiao H Y， Liu L G and Zhang J Y. 2022. HeadNeRF： a realtime NeRF-based parametric head model//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 20342-20352 ［DOI： 10.1109/CVPR52688.2022.01973http://dx.doi.org/10.1109/CVPR52688.2022.01973］
Howard A G， Zhu M L， Chen B， Kalenichenko D， Wang W J， Weyand T， Andreetto M and Adam H. 2017. MobileNets： efficient convolutional neural networks for mobile vision applications ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/1704.04861.pdfhttps://arxiv.org/pdf/1704.04861.pdf
Huang G B， Ramesh M A， Berg T L and Learned-Miller E. 2007. Labeled Faces in the Wild： A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts
InsightFace. ［EB/OL］. ［2024-06-06］. https://github.com/deepinsight/insightfacehttps://github.com/deepinsight/insightface
Jackson A S， Bulat A， Argyriou V and Tzimiropoulos G. 2017. Large pose 3D face reconstruction from a single Image via direct volumetric CNN regression//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 1031-1039 ［DOI： 10.1109/ICCV.2017.117http://dx.doi.org/10.1109/ICCV.2017.117］
Jing Y P， Lu X Q and Gao S. 2021. 3D face recognition： a survey ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/2108.11082.pdfhttps://arxiv.org/pdf/2108.11082.pdf
Kang M J， Choe J， Ha H， Jeon H G， Im S， Kweon I S and Yoon K J. 2022. Facial depth and normal estimation using single dual-pixel camera//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 181-200 ［DOI： 10.1007/978-3-031-20074-8_11http://dx.doi.org/10.1007/978-3-031-20074-8_11］
Karras T， Aila T， Laine S and Lehtinen J. 2018. Progressive growing of GANs for improved quality， stability， and variation ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/1710.10196.pdfhttps://arxiv.org/pdf/1710.10196.pdf
Karras T， Laine S and Aila T. 2019. A style-based generator architecture for generative adversarial networks//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 4396-4405 ［DOI： 10.1109/CVPR.2019.00453http://dx.doi.org/10.1109/CVPR.2019.00453］
Karras T， Laine S， Aittala M， Hellsten J， Lehtinen J and Aila T. 2020. Analyzing and improving the image quality of styleGAN//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 8107-8116 ［DOI： 10.1109/CVPR42600.2020.00813http://dx.doi.org/10.1109/CVPR42600.2020.00813］
Kipf T N and Welling M. 2017. Semi-supervised classification with graph convolutional networks ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/1609.02907.pdfhttps://arxiv.org/pdf/1609.02907.pdf
Konica Minolta Vivid 910. 2024. ［EB/OL］. ［2024-06-06］. https://www.konicaminolta.com.cn/instruments/download/catalog/pdf/vivid910_C.pdfhttps://www.konicaminolta.com.cn/instruments/download/catalog/pdf/vivid910_C.pdf
Krizhevsky A， Sutskever I and Hinton G E. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM， 60（6）： 84-90 ［DOI： 10.1145/3065386http://dx.doi.org/10.1145/3065386］
Lattas A， Lin Y M， Kannan J， Ozturk E， Filipi L， Guarnera G C， Chawla G and Ghosh A. 2022a. Practical and scalable desktop-based high-quality facial capture//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 522-537 ［DOI： 10.1007/978-3-031-20068-7_30http://dx.doi.org/10.1007/978-3-031-20068-7_30］
Lattas A， Moschoglou S， Gecer B， Ploumpis S， Triantafyllou V， Ghosh A and Zafeiriou S. 2020. AvatarMe： realistically renderable 3D facial reconstruction “in-the-wild”//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 757-766 ［DOI： 10.1109/CVPR42600.2020.00084http://dx.doi.org/10.1109/CVPR42600.2020.00084］
Lattas A， Moschoglou S， Ploumpis S， Gecer B， Deng J K and Zafeiriou S. 2023. FitMe： deep photorealistic 3D morphable model avatars//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 8629-8640 ［DOI： 10.1109/CVPR52729.2023.00834http://dx.doi.org/10.1109/CVPR52729.2023.00834］
Lattas A， Moschoglou S， Ploumpis S， Gecer B， Ghosh A and Zafeiriou S. 2022b. AvatarMe++： facial shape and BRDF inference with photorealistic rendering-aware GANs. IEEE Transactions on Pattern Analysis and Machine Intelligence， 44（12）： 9269-9284 ［DOI： 10.1109/TPAMI.2021.3125598http://dx.doi.org/10.1109/TPAMI.2021.3125598］
Lee C H， Liu Z W， Wu L Y and Luo P. 2020a. MaskGAN： towards diverse and interactive facial image manipulation//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 5548-5557 ［DOI： 10.1109/CVPR42600.2020.00559http://dx.doi.org/10.1109/CVPR42600.2020.00559］
Lee G H and Lee S W. 2020b. Uncertainty-aware mesh decoder for high fidelity 3D face reconstruction//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 6099-6108 ［DOI： 10.1109/CVPR42600.2020.00614http://dx.doi.org/10.1109/CVPR42600.2020.00614］
Lei B W， Ren J Q， Feng M Y， Cui M M and Xie X S. 2023. A hierarchical representation network for accurate and detailed face reconstruction from in-the-wild images//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 394-403 ［DOI： 10.1109/CVPR52729.2023.00046http://dx.doi.org/10.1109/CVPR52729.2023.00046］
Li C L， Morel-Forster A， Vetter T， Egger B and Kortylewski A. 2023a. Robust model-based face reconstruction through weakly-supervised outlier segmentation//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 372-381 ［DOI： 10.1109/CVPR52729.2023.00044http://dx.doi.org/10.1109/CVPR52729.2023.00044］
Li T Y， Bolkart T， Black M J， Li H and Romero J. 2017. Learning a model of facial shape and expression from 4D scans. ACM Transactions on Graphics， 36（6）： #194 ［DOI： 10.1145/3130800.3130813http://dx.doi.org/10.1145/3130800.3130813］
Li T Y， Liu S C， Bolkart T， Liu J Y， Li H and Zhao Y J. 2021. Topologically consistent multi-view face inference using volumetric sampling//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 3804-3814 ［DOI： 10.1109/ICCV48922.2021.00380http://dx.doi.org/10.1109/ICCV48922.2021.00380］
Li Y， Hao Q， Hu J G， Pan X M， Li Z C and Cui Z. 2023b. 3D3M： 3D modulated morphable model for monocular face reconstruction. IEEE Transactions on Multimedia， 25： 6642-6652 ［DOI： 10.1109/TMM.2022.3212282http://dx.doi.org/10.1109/TMM.2022.3212282］
Light Stage. ［EB/OL］. ［2024-06-06］. https://vgl.ict.usc.edu/LightStages/https://vgl.ict.usc.edu/LightStages/
Light Field Stage. ［EB/OL］. ［2024-01-24］. https://deemos.com/https://deemos.com/
Lin C， Nagano K， Kautz J， Chan E， Iqbal U， Guibas L， Wetzstein G and Khamis S. 2023. Single-shot implicit morphable faces with consistent texture parameterization//Proceedings of 2023 ACM SIGGRAPH Conference Proceedings. Los Angeles， USA： ACM： 83 ［DOI： 10.1145/3588432.3591494］
Lin J K， Yuan Y， Shao T J and Zhou K. 2020. Towards high-fidelity 3D face reconstruction from in-the-wild images using graph convolutional networks//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 5890-5899 ［DOI： 10.1109/CVPR42600.2020.00593http://dx.doi.org/10.1109/CVPR42600.2020.00593］
Lin T Y， Dollar P， Girshick R， He K M， Hariharan B and Belongie S. 2017. Feature pyramid networks for object detection//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 936-944 ［DOI： 10.1109/CVPR.2017.106http://dx.doi.org/10.1109/CVPR.2017.106］
Ling J W， Wang Z B， Lu M， Wang Q， Qian C and Xu F. 2022. Structure-aware editable morphable model for 3D facial detail animation and manipulation//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 249-267 ［DOI： 10.1007/978-3-031-20062-5_15http://dx.doi.org/10.1007/978-3-031-20062-5_15］
Liu X， Xu Y H， Wu Q Y， Zhou H， Wu W and Zhou B L. 2022. Semantic-aware implicit neural audio-driven video portrait generation//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 106-125 ［DOI： 10.1007/978-3-031-19836-6_7http://dx.doi.org/10.1007/978-3-031-19836-6_7］
Liu Z W， Luo P， Wang X G and Tang X O. 2015. Deep learning face attributes in the wild//Proceedings of 2015 International Conference on Computer Vision. Santiago， Chile： IEEE： 3730-3738 ［DOI： 10.1109/ICCV.2015.425http://dx.doi.org/10.1109/ICCV.2015.425］
Luo C W， Yu J， Yu L Y， Li Y L and Wang S J. 2021. Overview of research progress on 3-D face recognition. Journal of Tsinghua University （Science and Technology）， 61（1）： 77-88
罗常伟， 於俊， 于灵云， 李亚利， 王生进. 2021. 三维人脸识别研究进展综述. 清华大学学报（自然科学版）， 61（1）： 77-88. ［DOI： 10.16511/j.cnki.qhdxxb.2020.21.016http://dx.doi.org/10.16511/j.cnki.qhdxxb.2020.21.016］
Ma W C， Hawkins T， Peers P， Chabert C F， Weiss M and Debevec P. 2007. Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination//Proceedings of the 18th Eurographics Conference on Rendering Techniques. Grenoble， France： Eurographics Association： 183-194
Mallikarjun B R， Tewari A， Seidel H P， Elgharib M and Theobalt C. 2021. Learning complete 3D morphable face models from images and videos//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 3360-3370 ［DOI： 10.1109/CVPR46437.2021.00337http://dx.doi.org/10.1109/CVPR46437.2021.00337］
Medusa Facial Capture System. 2024 ［EB/OL］. ［2024-06-06］. https://la.disneyresearch.com/medusa/https://la.disneyresearch.com/medusa/
Mildenhall B， Srinivasan P P， Tancik M， Barron J T， Ramamoorthi R and Ng R. 2020. NeRF： representing scenes as neural radiance fields for view synthesis//Proceedings of the 16th European Conference on Computer Vision. Dublin， Ireland： Springer： 405-421 ［DOI： 10.1007/978-3-030-58452-8_24http://dx.doi.org/10.1007/978-3-030-58452-8_24］
Mollahosseini A， Hasani B and Mahoor M H. 2019. Affectnet： a database for facial expression， valence， and arousal computing in the wild. IEEE Transactions on Affective Computing， 10（1）： 18-31 ［DOI： 10.1109/TAFFC.2017.2740923http://dx.doi.org/10.1109/TAFFC.2017.2740923］
Morales A， Piella G and Sukno F M. 2021. Survey on 3D face reconstruction from uncalibrated images. Computer Science Review， 40： #100400 ［DOI： 10.1016/j.cosrev.2021.100400http://dx.doi.org/10.1016/j.cosrev.2021.100400］
Müller T， Evans A， Schied C and Keller A. 2022. Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics， 41（4）： #102 ［DOI： 10.1145/3528223.3530127http://dx.doi.org/10.1145/3528223.3530127］
Nagrani A， Chung J S and Zisserman A. 2017. VoxCeleb： a large-scale speaker identification dataset//Interspeech 2017. Stockholm， Sweden： ［s.n.］： 2616-2620 ［DOI： 10.21437/Interspeech.2017-950http://dx.doi.org/10.21437/Interspeech.2017-950］
Newell A， Yang K Y and Deng J. 2016. Stacked hourglass networks for human pose estimation//Proceedings of the 14th European Conference on Computer Vision. Amsterdam， the Netherlands： Springer： 483-499 ［DOI： 10.1007/978-3-319-46484-8_29http://dx.doi.org/10.1007/978-3-319-46484-8_29］
NextFace. ［EB/OL］. ［2024-06-06］. https://github.com/abdallahdib/NextFacehttps://github.com/abdallahdib/NextFace
Pan X G， Tewari A， Liu L J and Theobalt C. 2022. GAN2X： non-lambertian inverse rendering of image GANs//Proceedings of 2022 International Conference on 3D Vision. Prague， Czech Republic： IEEE： 711-721 ［DOI： 10.1109/3DV57658.2022.00081http://dx.doi.org/10.1109/3DV57658.2022.00081］
Papaioannou A， Gecer B， Cheng S Y， Chrysos G， Deng J K， Fotiadou E， Kampouris C， Kollias D， Moschoglou S， Songsri-In K， Ploumpis S， Trigeorgis G， Tzirakis P， Ververas E， Zhou Y X， Ponniah A， Roussos A and Zafeiriou S. 2022. MimicME： a large scale diverse 4D database for facial expression analysis//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 467-484 ［DOI： 10.1007/978-3-031-20074-8_27http://dx.doi.org/10.1007/978-3-031-20074-8_27］
Parkhi O M， Vedaldi A and Zisserman A. 2015. Deep face recognition//Proceedings of 2015 British Machine Vision Conference. IEEE： 41： 1-12
Paysan P， Knothe R， Amberg B， Romdhani S and Vetter T. 2009. A 3D face model for pose and illumination invariant face recognition//Proceedings of the 6th IEEE International Conference on Advanced Video and Signal based Surveillance. Genova， Italy： IEEE： 296-301 ［DOI： 10.1109/AVSS.2009.58http://dx.doi.org/10.1109/AVSS.2009.58］
Peng Z Q， Wu H Y， Song Z B， Xu H， Zhu X Y， He J， Liu H Y and Fan Z X. 2023. EmoTalk： speech-driven emotional disentanglement for 3D face animation ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/2303.11089.pdfhttps://arxiv.org/pdf/2303.11089.pdf
Phillips P J， Flynn P J， Scruggs T， Bowyer K W， Chang J， Hoffman K， Marques J， Min J and Worek W. 2005. Overview of the face recognition grand challenge//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego， USA： IEEE： 947-954 ［DOI： 10.1109/CVPR.2005.268http://dx.doi.org/10.1109/CVPR.2005.268］
Piao J T， Sun K Q， Wang Q， Lin K Y and Li H S. 2021. Inverting generative adversarial renderer for face reconstruction//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 15614-15623 ［DOI： 10.1109/CVPR46437.2021.01536http://dx.doi.org/10.1109/CVPR46437.2021.01536］
Pillai R K， Jeni L A， Yang H Y， Zhang Z， Yin L J and Cohn J F. 2019. The 2nd 3D face alignment in the wild challenge （3DFAW-video）： dense reconstruction from video//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop. Seoul， Korea （South）： IEEE： 3082-3089 ［DOI： 10.1109/ICCVW.2019.00371http://dx.doi.org/10.1109/ICCVW.2019.00371］
Ploumpis S， Ververas E， O’Sullivan E， Moschoglou S， Wang H Y， Pears N， Smith W A P， Gecer B and Zafeiriou S. 2021. Towards a complete 3D morphable model of the human head. IEEE Transactions on Pattern Analysis and Machine Intelligence， 43（11）： 4142-4160 ［DOI： 10.1109/TPAMI.2020.2991150http://dx.doi.org/10.1109/TPAMI.2020.2991150］
Ploumpis S， Wang H Y， Pears N， Smith W A P and Zafeiriou S. 2019. Combining 3D morphable models： a large scale face-and-head model//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 10926-10935 ［DOI： 10.1109/CVPR.2019.01119http://dx.doi.org/10.1109/CVPR.2019.01119］
Principi F， Berretti S， Ferrari C， Otberdout N， Daoudi M and Del Bimbo A. 2023. The florence 4D facial expression dataset//Proceedings of the 17th IEEE International Conference on Automatic Face and Gesture Recognition. Waikoloa Beach， USA： ACM： 1-6 ［DOI： 10.1109/FG57933.2023.10042606http://dx.doi.org/10.1109/FG57933.2023.10042606］
Rai A， Gupta H， Pandey A， Carrasco F V， Takagi S J， Aubel A， Kim D， Prakash A and de la Torre F. 2023. Towards realistic generative 3D face models ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/2304.12483.pdfhttps://arxiv.org/pdf/2304.12483.pdf
Ramon E， Triginer G， Escur J， Pumarola A， Garcia J， Giró-i-Nieto X and Moreno-Noguer F. 2021. H3D-net： few-shot high-fidelity 3D head reconstruction//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 5600-5609 ［DOI： 10.1109/ICCV48922.2021.00557http://dx.doi.org/10.1109/ICCV48922.2021.00557］
Ranjan A， Bolkart T， Sanyal S and Black M J. 2018. Generating 3D faces using convolutional mesh autoencoders//Proceedings of the 15th European Conference on Computer Vision. Munich， Germany： Springer： 725-741 ［DOI： 10.1007/978-3-030-01219-9_43http://dx.doi.org/10.1007/978-3-030-01219-9_43］
Ren X Y， Lattas A， Gecer B， Deng J K， Ma C and Yang X K. 2023. Facial geometric detail recovery via implicit representation//Proceedings of the 17th IEEE International Conference on Automatic Face and Gesture Recognition. Waikoloa Beach， USA： IEEE： 1-8 ［DOI： 10.1109/FG57933.2023.10042505http://dx.doi.org/10.1109/FG57933.2023.10042505］
Richard A， Zollhöfer M， Wen Y D， de la Torre F and Sheikh Y. 2021. MeshTalk： 3D face animation from speech using cross-modality disentanglement//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 1153-1162 ［DOI： 10.1109/ICCV48922.2021.00121http://dx.doi.org/10.1109/ICCV48922.2021.00121］
Richard A. N， Shahram I， Otmar H， David M， David， K， Andrew J D， Pushmeet K， Jamie S， Steve H and Andrew F. 2011. KinectFusion： real-time dense surface mapping and tracking//Proceedings of the 10th IEEE International Symposium on Mixed and Augmented Reality. Basel， Switzerland： IEEE： 127-136 ［DOI： 10.1109/ISMAR.2011. 6092378http://dx.doi.org/10.1109/ISMAR.2011.6092378］
Riviere J， Gotardo P， Bradley D， Ghosh A and Beeler T. 2020. Single-shot high-quality facial geometry and skin appearance capture. ACM Transactions on Graphics， 39（4）： #81 ［DOI： 10.1145/3386569.3392464http://dx.doi.org/10.1145/3386569.3392464］
Ronneberger O， Fischer P and Brox T. 2015. U-net： convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich， Germany： Springer： 234-241 ［DOI： 10.1007/978-3-319-24574-4_28http://dx.doi.org/10.1007/978-3-319-24574-4_28］
Rothe R， Timofte R and Van Gool L. 2015. DEX： deep expectation of apparent age from a single image//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop. Santiago， Chile： IEEE： 252-257 ［DOI： 10.1109/ICCVW.2015.41http://dx.doi.org/10.1109/ICCVW.2015.41］
Ruan Z Y， Zou C Q， Wu L H， Wu G S and Wang L M. 2021. SADRNet： self-aligned dual face regression networks for robust 3D dense face alignment and reconstruction. IEEE Transactions on Image Processing， 30： 5793-5806 ［DOI： 10.1109/tip.2021.3087397http://dx.doi.org/10.1109/tip.2021.3087397］
Sanyal S， Bolkart T， Feng H W and Black M J. 2019. Learning to regress 3D face shape and expression from an image without 3D supervision//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 7755-7764 ［DOI： 10.1109/CVPR.2019.00795http://dx.doi.org/10.1109/CVPR.2019.00795］
Savran A， Alyüz N， Dibeklioğlu H， Çeliktutan O， Gökberk B， Sankur B and Akarun L. 2008. Bosphorus database for 3D face analysis//Proceedings of the 1st European Workshop on Biometrics and Identity Management. Roskilde， Denmark： Springer： 47-56 ［DOI： 10.1007/978-3-540-89991-4_6http://dx.doi.org/10.1007/978-3-540-89991-4_6］
Schroff F， Kalenichenko D and Philbin J. 2015. FaceNet： a unified embedding for face recognition and clustering//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston， USA： IEEE： 815-823 ［DOI： 10.1109/CVPR.2015.7298682http://dx.doi.org/10.1109/CVPR.2015.7298682］
Shang J X， Shen T W， Li S W， Zhou L， Zhen M M， Fang T and Quan L. 2020. Self-supervised monocular 3D face reconstruction by occlusion-aware multi-view geometry consistency//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 53-70 ［DOI： 10.1007/978-3-030-58555-6_4http://dx.doi.org/10.1007/978-3-030-58555-6_4］
Sharma S and Kumar V. 2022. 3D face reconstruction in deep learning era： a survey. Archives of Computational Methods in Engineering， 29（5）： 3475-3507 ［DOI： 10.1007/s11831-021-09705-4http://dx.doi.org/10.1007/s11831-021-09705-4］
Shen Y J， Gu J J， Tang X O and Zhou B L. 2020. Interpreting the latent space of GANs for semantic face editing//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 9240-9249 ［DOI： 10.1109/CVPR42600.2020.00926http://dx.doi.org/10.1109/CVPR42600.2020.00926］
Sitzmann V， Martel J N P， Bergman A W， Lindell D B and Wetzstein G. 2020. Implicit neural representations with periodic activation functions//Proceedings of the 34th International Conference on Neural Information Processing Systems. Vancouver， Canada： Curran Associates Inc.： 7462-7473
Smith W A P， Seck A， Dee H， Tiddeman B， Tenenbaum J B and Egger B. 2020. A morphable face albedo model//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 5010-5019 ［DOI： 10.1109/CVPR42600.2020.00506http://dx.doi.org/10.1109/CVPR42600.2020.00506］
Sun Z N， He R， Wang L， Kan M N， Feng J J， Zheng F， Zheng W S， Zuo W M， Kang W X， Deng H W， Zhang J， Han H， Shan S G， Wang Y L， Ru Y W， Zhu Y H， Liu Y F and He Y. 2021. Overview of biometrics research. Journal of Image and Graphics， 26（6）： 1254-1329
孙哲南， 赫然， 王亮， 阚美娜， 冯建江， 郑方， 郑伟诗， 左旺孟， 康文雄， 邓伟洪， 张杰， 韩琥， 山世光， 王云龙， 茹一伟， 朱宇豪， 刘云帆， 何勇. 2021. 生物特征识别学科发展报告. 中国图象图形学报， 26（6）： 1254-1329 ［DOI： 10.11834/jig.210078http://dx.doi.org/10.11834/jig.210078］
Tewari A， Bernard F， Garrido P， Bharaj G， Elgharib M， Seidel H P， Pérez P， Zollhöfer M and Theobalt C. 2019. FML： face model learning from videos//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 10804-10814 ［DOI： 10.1109/CVPR.2019.01107http://dx.doi.org/10.1109/CVPR.2019.01107］
Tewari A， Zollhöfer M， Garrido P， Bernard F， Kim H， Perez P and Theobalt C. 2018. Self-supervised multi-level face model learning for monocular reconstruction at over 250 Hz//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 2549-2559 ［DOI： 10.1109/CVPR.2018.00270http://dx.doi.org/10.1109/CVPR.2018.00270］
Tewari A， Zollhofer M， Kim H， Garrido P， Bernard F， Perez P and Theobalt C. 2017. MoFA： model-based deep convolutional face autoencoder for unsupervised monocular reconstruction//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 3735-3744 ［DOI： 10.1109/ICCV.2017.401http://dx.doi.org/10.1109/ICCV.2017.401］
Thies J， Elgharib M， Tewari A， Theobalt C and Nießner M. 2020. Neural voice puppetry： audio-driven facial reenactment//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 716-731 ［DOI： 10.1007/978-3-030-58517-4_42http://dx.doi.org/10.1007/978-3-030-58517-4_42］
Thies J， Zollhofer M， Stamminger M， Theobalt C and Nießner M. 2016. Face2Face： real-time face capture and reenactment of RGB videos//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 2387-2395 ［DOI： 10.1109/CVPR.2016.262http://dx.doi.org/10.1109/CVPR.2016.262］
Tran A T， Hassner T， Masi I， Paz E， Nirkin Y and Medioni G. 2018. Extreme 3D face reconstruction： seeing through occlusions//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 3935-3944 ［DOI： 10.1109/CVPR.2018.00414http://dx.doi.org/10.1109/CVPR.2018.00414］
Tran L and Liu X M. 2018. Nonlinear 3D face morphable model//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 7346-7355 ［DOI： 10.1109/CVPR.2018.00767http://dx.doi.org/10.1109/CVPR.2018.00767］
Tu X G， Zhao J， Xie M， Jiang Z H， Balamurugan A， Luo Y， Zhao Y， He L X， Ma Z and Feng J S. 2021. 3D face reconstruction from a single image assisted by 2D face images in the wild. IEEE Transactions on Multimedia， 23： 1160-1172 ［DOI： 10.1109/TMM.2020.2993962http://dx.doi.org/10.1109/TMM.2020.2993962］
Vaswani A， Shazeer N， Parmar N， Uszkoreit J， Jones L， Gomez A N， Kaiser Ł and Polosukhin I. 2017. Attention is all you need//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach， USA： Curran Associates Inc.： 6000-6010
Wang J T and Li H B. 2023. Review of single-image 3D face reconstruction methods. Computer Engineering and Applications， 59（17）： 1-21
王静婷， 李慧斌. 2023. 单幅图像三维人脸重建方法综述. 计算机工程与应用， 59（17）： 1-21 ［DOI： 10.3778/j.issn.1002-8331.2210-0041http://dx.doi.org/10.3778/j.issn.1002-8331.2210-0041］
Wang L Z， Chen Z Y， Yu T， Ma C G， Li L and Liu Y B. 2022. FaceVerse： a fine-grained and detail-controllable 3D face morphable model from a hybrid dataset//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 20301-20310 ［DOI： 10.1109/CVPR52688.2022.01969http://dx.doi.org/10.1109/CVPR52688.2022.01969］
Wang M， Deng W H， Hu J N， Tao X Q and Huang Y H. 2019. Racial faces in the wild： reducing racial bias by information maximization adaptation network//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 692-702 ［DOI： 10.1109/ICCV.2019.00078http://dx.doi.org/10.1109/ICCV.2019.00078］
Wang T C， Liu M Y， Zhu J Y， Tao A， Kautz J and Catanzaro B. 2018. High-resolution image synthesis and semantic manipulation with conditional GANs//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 8798-8807 ［DOI： 10.1109/CVPR.2018.00917http://dx.doi.org/10.1109/CVPR.2018.00917］
Wang X Y， Guo Y D， Deng B L and Zhang J Y. 2020. Lightweight photometric stereo for facial details recovery//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 737-746 ［DOI： 10.1109/CVPR42600.2020.00082http://dx.doi.org/10.1109/CVPR42600.2020.00082］
Wen Y D， Liu W Y， Raj B and Singh R. 2021. Self-supervised 3D face reconstruction via conditional estimation//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 13269-13278 ［DOI： 10.1109/ICCV48922.2021.01304http://dx.doi.org/10.1109/ICCV48922.2021.01304］
Wenger A， Gardner A， Tchou C， Unger J， Hawkins T and Debevec P. 2005. Performance relighting and reflectance transformation with time-multiplexed illumination. ACM Transactions on Graphics， 24（3）： 756-764 ［DOI： 10.1145/1073204.1073258http://dx.doi.org/10.1145/1073204.1073258］
Wood E， Baltrušaitis T， Hewitt C， Johnson M， Shen J J， Milosavljević N， Wilde D， Garbin S， Sharp T， Stojiljković I， Cashman T and Valentin J. 2022. 3D face reconstruction with dense landmarks//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 160-177 ［DOI： 10.1007/978-3-031-19778-9_10http://dx.doi.org/10.1007/978-3-031-19778-9_10］
Wu C Y， Hsu C C and Neumann U. 2022. Cross-modal perceptionist： can face geometry be gleaned from voices?//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 10442-10451 ［DOI： 10.1109/CVPR52688.2022.01020http://dx.doi.org/10.1109/CVPR52688.2022.01020］
Wu C Y， Xu Q G and Neumann U. 2021. Synergy between 3DMM and 3D landmarks for accurate 3D facial geometry//Proceedings of 2021 International Conference on 3D Vision. London， UK： IEEE： 453-463 ［DOI： 10.1109/3DV53792.2021.00055http://dx.doi.org/10.1109/3DV53792.2021.00055］
Wu F Z， Bao L C， Chen Y J， Ling Y G， Song Y B， Li S N， Ngan K N and Liu W. 2019. MVF-net： multi-view 3D face morphable model regression//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 959-968 ［DOI： 10.1109/CVPR.2019.00105http://dx.doi.org/10.1109/CVPR.2019.00105］
Wu H Z， Jia J， Xing J L， Xu H W， Wang X Y and Wang J. 2023a. MMFace4D： a large-scale multi-modal 4D face dataset for audio-driven 3D face animation ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/2303.09797.pdfhttps://arxiv.org/pdf/2303.09797.pdf
Wu M H， Zhu H， Huang L J， Zhuang Y Y， Lu Y X and Cao X. 2023b. High-fidelity 3D face generation from natural language descriptions//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 4521-4530 ［DOI： 10.1109/CVPR52729.2023.00439http://dx.doi.org/10.1109/CVPR52729.2023.00439］
Wu S J， Yan Y C， Li Y H， Cheng Y H， Zhu W H， Gao K， Li X B and Zhai G T. 2023c. GANHead： towards generative animatable neural head avatars//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 437-447 ［DOI： 10.1109/CVPR52729.2023.00050http://dx.doi.org/10.1109/CVPR52729.2023.00050］
Wu S Z， Rupprecht C and Vedaldi A. 2020. Unsupervised learning of probably symmetric deformable 3D objects from images in the wild//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 1-10 ［DOI： 10.1109/CVPR42600.2020.00008http://dx.doi.org/10.1109/CVPR42600.2020.00008］
Xiao Y Z， Zhu H， Yang H T， Diao Z Y， Lu X J and Cao X. 2022. Detailed facial geometry recovery from multi-view images by learning an implicit function//Proceedings of the 36th AAAI Conference on Artificial Intelligence. Virtually： AAAI： 2839-2847 ［DOI： 10.1609/aaai.v36i3.20188http://dx.doi.org/10.1609/aaai.v36i3.20188］
Xing J B， Xia M H， Zhang Y C， Cun X， Wang J and Wong T T. 2023. CodeTalker： speech-driven 3D facial animation with discrete motion prior//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 12780-12790 ［DOI： 10.1109/CVPR52729.2023.01229http://dx.doi.org/10.1109/CVPR52729.2023.01229］
Xu C H， Tan T N， Li S， Wang Y H and Zhong C. 2006. Learning effective intrinsic features to boost 3D-based face recognition//Proceedings of the 9th European Conference on Computer Vision. Graz， Austria： Springer： 416-427 ［DOI： 10.1007/11744047_32http://dx.doi.org/10.1007/11744047_32］
Xu Y L， Wang L Z， Zhao X C， Zhang H W and Liu Y B. 2023. AvatarMAV： fast 3D head avatar reconstruction using motion-aware neural voxels//Proceedings of 2023 ACM SIGGRAPH Conference Proceedings. Los Angeles， USA： ACM： #47 ［DOI： 10.1145/3588432.3591567http://dx.doi.org/10.1145/3588432.3591567］
Yang H T， Zhu H， Wang Y R， Huang M K， Shen Q， Yang R G and Cao X. 2020. FaceScape： a large-scale high quality 3D face dataset and detailed riggable 3D face prediction//Proceedings of 2010 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 598-607 ［DOI： 10.1109/CVPR42600.2020.00068http://dx.doi.org/10.1109/CVPR42600.2020.00068］
Ye Y P， Song Z， Guo J G and Qiao Y. 2020. SIAT-3DFE： a high-resolution 3D facial expression dataset. IEEE Access， 8： 48205-48211 ［DOI： 10.1109/ACCESS.2020.2979518http://dx.doi.org/10.1109/ACCESS.2020.2979518］
Yenamandra T， Tewari A， Bernard F， Seidel H P， Elgharib M， Cremers D and Theobalt C. 2021. i3DMM： deep implicit 3D morphable model of human heads//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 12798-12808 ［DOI： 10.1109/CVPR46437.2021.01261http://dx.doi.org/10.1109/CVPR46437.2021.01261］
Yi D， Lei Z， Liao S C and Li S Z. 2014. Learning face representation from scratch ［EB/OL］. ［2024-06-06］. https://arxiv.org/pdf/1411.7923.pdfhttps://arxiv.org/pdf/1411.7923.pdf
Yi H W， Li C， Cao Q， Shen X Y， Li S， Wang G P and Tai Y W. 2019. MMFace： a multi-metric regression network for unconstrained face reconstruction//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 7655-7664 ［DOI： 10.1109/CVPR.2019.00785http://dx.doi.org/10.1109/CVPR.2019.00785］
Yin B C， Sun Y F， Wang C Z and Gai Y. 2009. BJUT-3D large scale 3D face database and information processing. Journal of Computer Research and Development， 46（6）： 1009-1018
尹宝才， 孙艳丰， 王成章， 盖赟. 2009. BJUT-3D三维人脸数据库及其处理技术. 计算机研究与发展， 46（6）： 1009-1018
Yin L J， Chen X C， Sun Y， Worm T and Reale M. 2008. A high-resolution 3D dynamic facial expression database//Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition. Amsterdam， Netherlands： IEEE： 1-6 ［DOI： 10.1109/AFGR.2008.4813324http://dx.doi.org/10.1109/AFGR.2008.4813324］
Yin L J， Wei X Z， Sun Y， Wang J and Rosato M J. 2006. A 3D facial expression database for facial behavior research//Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition. Southampton，UK： IEEE： 211-216 ［DOI： 10.1109/FGR.2006.6http://dx.doi.org/10.1109/FGR.2006.6］
Zeng X X， Peng X J and Qiao Y. 2019. DF2Net： a dense-fine-finer network for detailed 3D face reconstruction//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 2315-2324 ［DOI： 10.1109/ICCV.2019.00240http://dx.doi.org/10.1109/ICCV.2019.00240］
Zhang L W， Qiu Q W， Lin H Y， Zhang Q X， Shi C， Yang W， Shi Y， Yang S B， Xu L and Yu J Y. 2023a. DreamFace： progressive generation of animatable 3D faces under text guidance. ACM Transactions on Graphics， 42（4）： #138 ［DOI： 10.1145/3592094http://dx.doi.org/10.1145/3592094］
Zhang L W， Zeng C X， Zhang Q X， Lin H Y， Cao R X， Yang W， Xu L and Yu J Y. 2022a. Video-driven neural physically-based facial asset for production. ACM Transactions on Graphics， 41（6）： #208 ［DOI： 10.1145/3550454.3555445http://dx.doi.org/10.1145/3550454.3555445］
Zhang L W， Zhang Q X， Wu M Y， Yu J Y and Xu L. 2021a. Neural video portrait relighting in real-time via consistency modeling//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 782-792 ［DOI： 10.1109/ICCV48922.2021.00084http://dx.doi.org/10.1109/ICCV48922.2021.00084］
Zhang Y L， Li K P， Li K， Wang L C， Zhong B N and Fu Y. 2018. Image super-resolution using very deep residual channel attention networks//Proceedings of the 15th European Conference on Computer Vision. Munich， Germany： Springer： 294-310 ［DOI： 10.1007/978-3-030-01234-2_18http://dx.doi.org/10.1007/978-3-030-01234-2_18］
Zhang Z H， Liu W， Liu G D， Song L M， Qu Y F， Li X D and Wei Z Z. 2021. Overview of the development and application of 3D vision measurement technology. Journal of Image and Graphics， 26（6）： 1483-1502
张宗华， 刘巍， 刘国栋， 宋丽梅， 屈玉福， 李旭东， 魏振忠. 2021. 三维视觉测量技术及应用进展. 中国图象图形学报， 26（6）： 1483-1502 ［DOI： 10.11834/jig.200841http://dx.doi.org/10.11834/jig.200841］
Zhang Z M， Li L C， Ding Y and Fan C J. 2021b. Flow-guided one-shot talking face generation with a high-resolution audio-visual dataset//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 3660-3669 ［DOI： 10.1109/CVPR46437.2021.00366http://dx.doi.org/10.1109/CVPR46437.2021.00366］
Zhang Z Y， Chen R W， Cao W J， Tai Y and Wang C J. 2023b. Learning neural proto-face field for disentangled 3D face modeling in the wild//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 382-393 ［DOI： 10.1109/CVPR52729.2023.00045http://dx.doi.org/10.1109/CVPR52729.2023.00045］
Zhang Z Y， Ge Y H， Chen R W， Tai Y， Yan Y， Yang J， Wang C J， Li J L and Huang F Y. 2021c. Learning to aggregate and personalize 3D face from in-the-wild photo collection//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 14209-14219 ［DOI： 10.1109/CVPR46437.2021.01399http://dx.doi.org/10.1109/CVPR46437.2021.01399］
Zhang Z Y， Ge Y H， Tai Y， Cao W J， Chen R W， Liu K L， Tang H， Huang X M， Wang C J， Xie Z F and Huang D J. 2022b. Physically-guided disentangled implicit rendering for 3D face modeling//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 20321-20331 ［DOI： 10.1109/CVPR52688.2022.01971http://dx.doi.org/10.1109/CVPR52688.2022.01971］
Zhang Z Y， Ge Y H， Tai Y， Huang X M， Wang C J， Tang H， Huang D J and Xie Z F. 2022c. Learning to restore 3D face from in-the-wild degraded images//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 4227-4237 ［DOI： 10.1109/CVPR52688.2022.00420http://dx.doi.org/10.1109/CVPR52688.2022.00420］
Zheng M W， Yang H Y， Huang D and Chen L M. 2022a. ImFace： a nonlinear 3D morphable face model with implicit neural representations//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 20311-20320 ［DOI： 10.1109/CVPR52688.2022.01970http://dx.doi.org/10.1109/CVPR52688.2022.01970］
Zheng M W， Zhang H Y， Yang H Y and Huang D. 2023. NeuFace： realistic 3D neural face rendering from multi-view images//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 16868-16877 ［DOI： 10.1109/CVPR52729.2023.01618http://dx.doi.org/10.1109/CVPR52729.2023.01618］
Zheng Y F， Abrevaya V F， Buhler M C， Chen X， Black M J and Hilliges O. 2022b. I M avatar： implicit morphable head avatars from videos//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 13535-13545 ［DOI： 10.1109/CVPR52688.2022.01318http://dx.doi.org/10.1109/CVPR52688.2022.01318］
Zhu W B， Wu H T， Chen Z Y， Vesdapunt N and Wang B Y. 2020a. ReDA： reinforced differentiable attribute for 3D face reconstruction//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 4957-4966 ［DOI： 10.1109/CVPR42600.2020.0050http://dx.doi.org/10.1109/CVPR42600.2020.0050］
Zhu X Y， Lei Z， Liu X M， Shi H L and Li S Z. 2016. Face alignment across large poses： a 3D solution//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 146-155 ［DOI： 10.1109/CVPR.2016.23http://dx.doi.org/10.1109/CVPR.2016.23］
Zhu X Y， Yang F， Huang D， Yu C， Wang H， Guo J Z， Lei Z and Li S Z. 2020b. Beyond 3DMM space： towards fine-grained 3D face reconstruction//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 343-358 ［DOI： 10.1007/978-3-030-58598-3_21http://dx.doi.org/10.1007/978-3-030-58598-3_21］
Zhuang Y Y， Zhu H， Sun X S and Cao X. 2022. MoFaNeRF： morphable facial neural radiance field//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 268-285 ［DOI： 10.1007/978-3-031-20062-5_16http://dx.doi.org/10.1007/978-3-031-20062-5_16］
Zielonka W， Bolkart T and Thies J. 2022. Towards metrical reconstruction of human faces//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 250-269 ［DOI： 10.1007/978-3-031-19778-9_15http://dx.doi.org/10.1007/978-3-031-19778-9_15］
Zielonka W， Bolkart T and Thies J. 2023. Instant volumetric head avatars//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 4574-4584 ［DOI： 10.1109/CVPR52729.2023.00444http://dx.doi.org/10.1109/CVPR52729.2023.00444］
Zollhöfer M， Thies J， Garrido P， Bradley D， Beeler T， Pérez P， Stamminger M， Nießner M and Theobalt C. 2018. State of the art on monocular 3D face reconstruction， tracking， and applications. Computer Graphics Forum， 37（2）： 523-550 ［DOI： 10.1111/cgf.13382http://dx.doi.org/10.1111/cgf.13382］
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