mha142/3DMM-and-3D-Face-reconstruction-and-manipulation

Paper list of 3D Face reconstruction and manipulation and relevant topics

3DMM-and-3D-Face-reconstruction-and-manipulation

Paper list of 3D Face reconstruction and manipulation

Survey

• Bernhard Egger, William A. P. Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhöfer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, Christian Theobalt, Volker Blanz, Thomas Vetter: 3D Morphable Face Models - Past, Present and Future. CoRR abs/1909.01815 (2019) [Paper]

• Michael Zollhöfer, Justus Thies, Pablo Garrido, Derek Bradley, Thabo Beeler, Patrick Pérez, Marc Stamminger, Matthias Nießner, Christian Theobalt: State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications. Comput. Graph. Forum 37(2): 523-550 (2018) [Paper][code]

Groups for 3D models and Graphic visions

• [MPII - Visual Computing and Artificial Intelligence Department]

Githubs

• https://github.com/3d-morphable-models/curated-list-of-awesome-3D-Morphable-Model-software-and-data

Researchers

• Prof. Dr. Christian Theobalt [Projects]
• Michael Zollhoefer [Publications]
• Dr Aaron S. Jackson [Research]
• Kun Zhou [Research]

Seminar

• 3D Morphable Models

Deep learning (VAE & Encoder-decoder models)

• Chi Nhan Duong, Khoa Luu, Kha Gia Quach, and Tien D. Bui. 2019. Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling. International Journal of Computer Vision 127, 5 (2019), 437–455.[Paper][Project][code]

• Victoria Fernández Abrevaya, Stefanie Wuhrer, and Edmond Boyer. 2018. Multilinear Autoencoder for 3D Face Model Learning. In Proc. IEEE Winter Conference on Applications of Computer Vision (WACV).

• James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, and Stefanos Zafeiriou. 2017. 3D face morphable models "In-The-Wild". In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 5464–5473.

• Stephen Lombardi, Jason Saragih, Tomas Simon, and Yaser Sheikh. 2018. Deep appearance models for face rendering. ACM Transactions on Graphics 37, 4 (2018), 68.

• Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. 2018. Generating 3D faces using Convolutional Mesh Autoencoders. In Proc. European Conference on Computer Vision (ECCV). 725–741.

• Yuxiang Zhou, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou. 2019. Dense 3D Face Decoding over 2500FPS: Joint Texture and Shape Convolutional Mesh Decoders. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Deep learning (GAN models)

• Ron Slossberg, Gil Shamai, and Ron Kimmel. 2018. High quality facial surface and texture synthesis via generative adversarial networks. In Proc. European Conference on Computer Vision (ECCV). 0–0.

• Victoria Fernández Abrevaya, Adnane Boukhayma, StefanieWuhrer, and Edmond Boyer. 2019. A Generative 3D Facial Model by Adversarial Training. In Proc. International Conference on Computer Vision (ICCV).

• Gil Shamai, Ron Slossberg, and Ron Kimmel. 2019. Synthesizing facial photometries and corresponding geometries using generative adversarial networks. arXiv preprint arXiv:1901.06551 (2019).

• Shiyang Cheng, Michael Bronstein, Yuxiang Zhou, Irene Kotsia, Maja Pantic, and Stefanos Zafeiriou. 2019. MeshGAN: Non-linear 3D Morphable Models of Faces. arXiv preprint arXiv:1903.10384 (2019).

Deep learning (Hybrid structures & Nonlinear 3DMM)

• Luan Tran and Xiaoming Liu. 2018. Nonlinear 3D Face Morphable Model. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT.

• Mengjiao Wang, Zhixin Shu, Shiyang Cheng, Yannis Panagakis, Dimitris Samaras, and Stefanos Zafeiriou. 2019. An Adversarial Neuro-Tensorial Approach for Learning Disentangled Representations. International Journal of Computer Vision 127 (2019), 743–762.

Self-supervised

• Ayush Tewari, Michael Zollhoefer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Theobalt Christian. 2017. MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction. In Proc. International Conference on Computer Vision (ICCV).

• Anh Tuan Tran, Tal Hassner, Iacopo Masi, and Gérard Medioni. 2017. Regressing robust and discriminative 3D morphable models with a very deep neural network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 5163–5172.

• Elad Richardson, Matan Sela, Roy Or-El, and Ron Kimmel. 2017. Learning detailed face reconstruction from a single image. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1259–1268.

• James Booth, Anastasios Roussos, Allan Ponniah, David Dunaway, and Stefanos Zafeiriou. 2018a. Large scale 3D morphable models. International Journal of Computer Vision 126, 2-4 (2018), 233–254.

• Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. 2019. Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2019).

• Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. 2018. Unsupervised training for 3d morphable model regression. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 8377–8386.

• Soubhik Sanyal, Timo Bolkart, Haiwen Feng, and Michael Black. 2019. Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

• Elad Richardson, Matan Sela, Roy Or-El, and Ron Kimmel. 2017. Learning detailed face reconstruction from a single image. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1259–1268.

• Soumyadip Sengupta, Angjoo Kanazawa, Carlos D Castillo, and David W Jacobs. 2018. SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wild’. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6296–6305.

• Anil Bas, Patrik Huber, William AP Smith, Muhammad Awais, and Josef Kittler. 2017a. 3D Morphable Models as Spatial Transformer Networks. In Proc. International Conference on Computer Vision (ICCV) Workshops. IEEE, 895–903.

High Fidelity, Face Texture, GAN Hallucination

• B. Gecer, S. Ploumpis, I. Kotsia, and S. Zafeiriou, “GANFIT: generative adversarial network fitting for high fidelity 3d face reconstruction,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, 2019, pp. 1155–1164.[Paper][code]

• Ron Slossberg, Gil Shamai, Ron Kimmel: High Quality Facial Surface and Texture Synthesis via Generative Adversarial Networks. ECCV Workshops (3) 2018: 498-513. [Paper]

• A. Chen, Z. Chen, G. Zhang, K. Mitchell, and J. Yu, “Photo-realistic facial details synthesis from single image,” in IEEE International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, 2019, pp. 9428–9438. [Paper][code]

• A. Tewari, M. Zollh ̈ofer, F. Bernard, P. Garrido, H. Kim, P. P ́erez, and C. Theobalt, “High-fidelity monocular face reconstruction based on anunsupervised model-based face autoencoder,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 357–370, 2020. [Online] [Project] [Paper]

• Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In IEEE Computer Vision and Pattern Recognition Workshops, 2019. [code]

• Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. Unsupervised training for 3d morphable model regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8377--8386.

Face reconstruction from videos

• Tewari, F. Bernard, P. Garrido, G. Bharaj, M. Elgharib, H. Seidel, P. P ́erez, M. Zollh ̈ofer, and C. Theobalt, “FML: face model learning from videos,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, 2019, pp. 10 812–10 822. [Paper][Project][[code]]

CVPR2019

• Z. Fan, X. Hu, C. Chen, and S. Peng, “Boosting local shape matching for dense 3d face correspondence,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 10944–10 954.[Paper][code]

• B. Gecer, S. Ploumpis, I. Kotsia, and S. Zafeiriou, “GANFIT: generative adversarial network fitting for high fidelity 3d face reconstruction,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, 2019, pp. 1155–1164.[Paper][code]

• Z. Geng, C. Cao, and S. Tulyakov, “3D Guided Fine-grained Face Manipulation,” in IEEE Conference on Computer Visionand Pattern Recognition, CVPR 2019, Long Beach, CA,USA, June 16-20, 2019, 2019, pp. 9821–9830.[Paper]

• Z. Jiang, Q. Wu, K. Chen, and J. Zhang, “Disentangled Representation Learning for 3D Face Shape,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA,June 16-20, 2019, 2019, pp. 11 957–11 966. [Paper]

• G. Mu, D. Huang, G. Hu, J. Sun, and Y. Wang, “Led3d:Alight weight and efficient deep approach to recognizing low-quality 3d faces,” in IEEE Conference on ComputerVision and Pattern Recognition, CVPR 2019, Long Beach, CA,USA, June 16-20, 2019, 2019, pp. 5773–5782.[Paper]

• G. Pavlakos, V. Choutas, N. Ghorbani, T. Bolkart, A. A. A. Osman,D. Tzionas, and M. J. Black, “Expressive body capture: 3d hands, face,and body from a single image,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA,June 16-20, 2019, 2019, pp. 10 975–10 985. [Paper]

• S. Ploumpis, H. Wang, N. E. Pears, W. A. P. Smith, andS. Zafeiriou, “Combining 3d morphable models: A large scale face-and-head model,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA,June 16-20, 2019, 2019, pp. 10 934–10 943. [Paper]

• S. Sanyal, T. Bolkart, H. Feng, and M. J. Black, “Learning to regress 3d face shape and expression from an image without 3d supervision,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA,USA, June 16-20, 2019, 2019, pp. 7763–7772. [Paper]

• L. Tran, F. Liu, and X. Liu, “Towards high-fidelity nonlinear 3d face morphable model,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA,USA, June 16-20, 2019, 2019, pp. 1126–1135. [Paper]

• F. Wu, L. Bao, Y. Chen, Y. Ling, Y. Song, S. Li, K. N.Ngan, and W. Liu, “MVF-Net: Multi-view 3d face morphablemodel regression,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA,June 16-20, 2019, 2019, pp.959–968. [Paper]

• Y. Zhou, J. Deng, I. Kotsia, and S. Zafeiriou, “Dense 3D Face Decoding over 2500FPS: Joint texture & shape convolutional mesh decoders,” in IEEE Conference on Computer Visionand Pattern Recognition, CVPR 2019, Long Beach, CA,USA, June 16-20, 2019, 2019, pp. 1097–1106. [Paper]

CVPR2018

• Feng Liu, Ronghang Zhu, Dan Zeng, Qijun Zhao, and Xiaoming Liu. “Disentangling features in 3d face shapes for joint face reconstruction and recognition.” In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 5216–5225, 2018.[Paper]

• Xuan Cao, Zhang Chen, Anpei Chen, Xin Chen, Shiying Li, and Jingyi Yu. Sparse photometric 3d face reconstruction guided by morphable models. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 4635–4644, 2018. [Paper]

• Syed Zulqarnain Gilani and Ajmal Mian. Learning from millions of 3d scans for large-scale 3d face recognition. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 1896–1905, 2018. [Paper]

• Hanbyul Joo, Tomas Simon, and Yaser Sheikh. Total capture: A 3d deformation model for tracking faces, hands, and bodies. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 8320–8329, 2018. [Paper]

• Amit Kumar and Rama Chellappa. Disentangling 3d pose in a dendritic CNN for unconstrained 2d face alignment. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 430–439, 2018. [Paper]

• Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, SaltLake City, UT, USA, June 18-22, 2018, pages 7346–7355, 2018. [Paper][code][Project]

• Anh Tuan Tran, Tal Hassner, Iacopo Masi, Eran Paz, Yuval Nirkin, and G ́erard G. Medioni. Extreme 3d face reconstruction: Seeing through occlusions.In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 3935–3944, 2018 [Paper]

CVPR2017

• Fabio Maninchedda, Martin R. Oswald, and Marc Pollefeys. Fast 3d reconstruction of faces with glasses. In IEEE Conference on Computer Vision andPattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 4608–4617, 2017.

• Pengfei Dou, Shishir K. Shah, and Ioannis A. Kakadiaris. End-to-end 3d face reconstruction with deep neural networks. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 1503–1512, 2017.

• James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, and Stefanos Zafeiriou. 3d face morphable models ”in-the-wild”. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 5464–5473, 2017.

ICCV2019

• A. Chen, Z. Chen, G. Zhang, K. Mitchell, and J. Yu, “Photo-realistic facial details synthesis from single image,” in IEEE InternationalConference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, 2019, pp. 9428–9438. [Online]. Available:https://doi.org/10.1109/ICCV.2019.00952

• X. Zeng, X. Peng, and Y. Qiao, “DF2Net: A dense-fine-finer network for detailed 3d face reconstruction,” in IEEE International Conferenceon Computer Vision, ICCV 2019, Seoul, Korea (South), October27 - November 2, 2019, 2019, pp. 2315–2324. [Paper]

• X. Yuan and I. K. Park, “Face de-occlusion using 3d morphablemodel and generative adversarial network,” in IEEE International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, 2019, pp. 10 061–10 070. [Paper]

• W. Ren, J. Yang, S. Deng, D. P. Wipf, X. Cao, and X. Tong, “Face video deblurring using 3d facial priors,” in IEEE International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, 2019, pp. 9387–9396. [Paper]

• J. Piao, C. Qian, and H. Li, “Semi-supervised monocular 3d face reconstruction with end-to-end shape-preserved domain transfer,” in IEEE International Conference on Computer Vision, ICCV 2019,Seoul, Korea (South), October 27 - November 2, 2019, 2019, pp. 9397–9406. [Paper]

• F. Liu, L. Tran, and X. Liu, “3d face modeling from diverse raw scan data,” in IEEE International Conference onComputer Vision, ICCV 2019, Seoul, Korea (South), October 27- November 2, 2019, 2019, pp. 9407–9417. [Paper][code]

ICCV2017

• S. Jackson, A. Bulat, V. Argyriou, and G. Tzimiropoulos, “Large pose 3d face reconstruction from a single image via direct volumetric CNNregression,” inIEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, 2017, pp. 1031–1039. [Online] [code]

ECCV2018

• Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. Generating 3d faces using convolutional mesh autoencoders. In Computer Vision- ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part III, pages 725–741, 2018. [Paper]

• Mingtao Feng, Syed Zulqarnain Gilani, Yaonan Wang, and Ajmal S. Mian. 3d face reconstruction from light field images: A model-free approach. In Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part X, pages 508–526, 2018. [Paper]

• Baris Gecer, Binod Bhattarai, Josef Kittler, and Tae-Kyun Kim. Semi-supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model. In Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, PartXI, pages 230–248, 2018. [Paper]

• Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. Joint 3d face reconstruction and dense alignment with position map regression network. In Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Part XIV, pages 557–574, 2018. [Paper][code]

• Siqi Liu, Xiangyuan Lan, and Pong C. Yuen. Remote photoplethysmography correspondence feature for 3d mask face presentation attack detection. In* Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018*, Part XVI, pages 577–594, 2018. [Paper]

• Zhenfeng Fan, Xiyuan Hu, Chen Chen, and Silong Peng. Dense semantic and topological correspondence of 3d faces without landmarks. In Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Part XVI, pages 541–558, 2018. [Paper]

CoRR

• [1] Jiangke Lin, Yi Yuan, Tianjia Shao, and Kun Zhou. Towards high-fidelity 3d face reconstruction from in-the-wild images using graph convolutional networks. CoRR, abs/2003.05653, 2020.

• [2] Baris Gecer, Alexander Lattas, Stylianos Ploumpis, Jiankang Deng, Athanasios Papaioannou, Stylianos Moschoglou, and Stefanos Zafeiriou. Synthesizing coupled 3d face modalities by trunk-branch generative adversarial networks. CoRR, abs/1909.02215, 2019.

• [3] Evangelos Ververas and Stefanos Zafeiriou. Slidergan: Synthesizing expressive face images by sliding 3d blendshape parameters.CoRR, abs/1908.09638,2019.

• [4] Yajing Chen, Fanzi Wu, Zeyu Wang, Yibing Song, Yonggen Ling, and Linchao Bao. Self-supervised learning of detailed 3d face reconstruction.CoRR,abs/1910.11791, 2019.

• [5] Guoxian Song, Jianfei Cai, Tat-Jen Cham, Jianmin Zheng, Juyong Zhang, and Henry Fuchs. Real-time 3d face-eye performance capture of a personwearing VR headset.CoRR, abs/1901.06765, 2019.

• [6] Luan Tran and Xiaoming Liu. On learning 3d face morphable model from in-the-wild images.CoRR, abs/1808.09560, 2018. [Paper]

• [7] Maur ́ıcio Sousa, Daniel Mendes, Rafael Kuffner dos Anjos, Daniel Sim ̃oes Lopes, and Joaquim A. Jorge. Negative space: Workspace awareness in 3dface-to-face remote collaboration.CoRR, abs/1910.03380, 2019.

• [8] Mehryar Emambakhsh and Adrian N. Evans. Nasal patches and curves for expression-robust 3d face recognition.CoRR, abs/1901.00206, 2019.

• [9] Shiyang Cheng, Michael M. Bronstein, Yuxiang Zhou, Irene Kotsia, Maja Pantic, and Stefanos Zafeiriou. Meshgan: Non-linear 3d morphable modelsof faces.CoRR, abs/1903.10384, 2019.

• [10] Shridhar Ravikumar. Lightweight markerless monocular face capture with 3d spatial priors.CoRR, abs/1901.05355, 2019.

• [11] Yao Luo, Xiaoguang Tu, and Mei Xie. Learning robust 3d face reconstruction and discriminative identity representation.CoRR, abs/1905.06505, 2019.

• [12] Xiaoguang Tu, Jian Zhao, Zihang Jiang, Yao Luo, Mei Xie, Yang Zhao, Linxiao He, Zheng Ma, and Jiashi Feng. Joint 3d face reconstruction and denseface alignment from A single image with 2d-assisted self-supervised learning.CoRR, abs/1903.09359, 2019.

• [13] Jianzhu Guo, Xiangyu Zhu, Jinchuan Xiao, Zhen Lei, Genxun Wan, and Stan Z. Li. Improving face anti-spoofing by 3d virtual synthesis.CoRR,abs/1901.00488, 2019.

• [14] Rafal Pilarczyk, Xin Chang, and Wladyslaw Skarbek. Human face expressions from images - 2d face geometry and 3d face local motion versus deep neural features.CoRR, abs/1901.11179, 2019.

• [15] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: a large-scale high quality 3d face datasetand detailed riggable 3d face prediction.CoRR, abs/2003.13989, 2020.

• [16] Xiaowei Yuan and In Kyu Park. Face de-occlusion using 3d morphable model and generative adversarial network.CoRR, abs/1904.06109, 2019.

• [17] Roberto Valle, Jos ́e Miguel Buenaposada, Antonio Vald ́es, and Luis Baumela. Face alignment using a 3d deeply-initialized ensemble of regression trees.CoRR, abs/1902.01831, 2019.

• [18] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed A. A. Osman, Dimitrios Tzionas, and Michael J. Black. Expressive bodycapture: 3d hands, face, and body from a single image. CoRR, abs/1904.05866, 2019.

• [19] Lei Jiang, Xiao-Jun Wu, and Josef Kittler. Dual attention mobdensenet(damdnet) for robust 3d face alignment. CoRR, abs/1908.11821, 2019.

• [20] Ziyu Zhang, Feipeng Da, and Yi Yu. Data-free point cloud network for 3d face recognition. CoRR, abs/1911.04731, 2019.

• [21] Stylianos Ploumpis, Haoyang Wang, Nick E. Pears, William A. P. Smith, and Stefanos Zafeiriou. Combining 3d morphable models: A large scaleface-and-head model.CoRR, abs/1903.03785, 2019.

• [22] Feng Liu, Dan Zeng, Jing Li, and Qijun Zhao. Cascaded regressor based 3d face reconstruction from a single arbitrary view image.CoRR, abs/1509.06161,2015.

• [23] Rinat Abdrashitov, Alec Jacobson, and Karan Singh. A system for efficient 3d printed stop-motion face animation.CoRR, abs/1907.10163, 2019.

• [24] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: Fromsingle image to image set.CoRR, abs/1903.08527, 2019.

• [25] Bernhard Egger, William A. P. Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollh ̈ofer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski,Sami Romdhani, Christian Theobalt, Volker Blanz, and Thomas Vetter. 3d morphable face models - past, present and future.CoRR, abs/1909.01815,2019.

• [26] Zhang Chen, Yu Ji, Mingyuan Zhou, Sing Bing Kang, and Jingyi Yu. 3d face reconstruction using color photometric stereo with uncalibrated near pointlights.CoRR, abs/1904.02605, 2019.

• [27] Ngoc-Trung Tran, Fakhr-Eddine Ababsa, Maurice Charbit, Jacques Feldmar, Dijana Petrovska-Delacr ́etaz, and G ́erard Chollet. 3d face pose and animationtracking via eigen-decomposition based bayesian approach.CoRR, abs/1908.11039, 2019.

• [28] Feng Liu, Luan Tran, and Xiaoming Liu. 3d face modeling from diverse raw scan data.CoRR, abs/1902.04943, 2019.

• [29] Lei Li, Zhaoqiang Xia, Xiaoyue Jiang, Yupeng Ma, Fabio Roli, and Xiaoyi Feng. 3d face mask presentation attack detection based on intrinsic imageanalysis.CoRR, abs/1903.11303, 2019.

• [30] Stylianos Moschoglou, Stylianos Ploumpis, Mihalis Nicolaou, Athanasios Papaioannou, and Stefanos Zafeiriou. 3dfacegan: Adversarial nets for 3d facerepresentation, generation, and translation.CoRR, abs/1905.00307, 2019.

• [31] Huawei Wei, Shuang Liang, and Yichen Wei. 3d dense face alignment via graph convolution networks. CoRR, abs/1904.05562, 2019.

• [32] Zipeng Ye, Ran Yi, Minjing Yu, Juyong Zhang, Yu-Kun Lai, and Yong-Jin Liu. 3d-carigan: An end-to-end solution to 3d caricature generation from face photos.CoRR, abs/2003.06841, 2020

TPAMI

• Feng Liu, Qijun Zhao, Xiaoming Liu, and Dan Zeng. Joint face alignment and 3d face reconstruction with application to face recognition. IEEE Trans.Pattern Anal. Mach. Intell., 42(3):664–678, 2020

• Yudong Guo, Juyong Zhang, Jianfei Cai, Boyi Jiang, Jianmin Zheng. CNN-Based Real-Time Dense Face Reconstruction with Inverse-Rendered Photo-Realistic Face Images. IEEE Trans. Pattern Anal. Mach. Intell. 41(6): 1294-1307 (2019)

• Jian Zhao, Lin Xiong, Jianshu Li, Junliang Xing, Shuicheng Yan, and Jiashi Feng. 3d-aided dual-agent gans for unconstrained face recognition.IEEE Trans. Pattern Anal. Mach. Intell., 41(10):2380–2394, 2019.

• James Booth, Anastasios Roussos, Evangelos Ververas, Epameinondas Antonakos, Stylianos Ploumpis, Yannis Panagakis, and Stefanos Zafeiriou. 3d reconstruction of “in-the-wild” faces in images and videos.IEEE Trans. Pattern Anal. Mach. Intell., 40(11):2638–2652, 2018.

• Joseph Roth, Yiying Tong, and Xiaoming Liu. Adaptive 3d face reconstruction from unconstrained photo collections. IEEE Trans. Pattern Anal. Mach.Intell., 39(11):2127–2141, 2017.

• Kangkan Wang, Xianwang Wang, Zhigeng Pan, and Kai Liu. A two-stage framework for 3d facereconstruction from RGBD images.IEEE Trans. Pattern Anal. Mach. Intell., 36(8):1493–1504, 2014.

• Hassen Drira, Boulbaba Ben Amor, Anuj Srivastava, Mohamed Daoudi, and Rim Slama. 3d face recognition under expressions, occlusions, and posevariations.IEEE Trans. Pattern Anal. Mach. Intell., 35(9):2270–2283, 2013.

• Oswald Aldrian and William A. P. Smith. Inverse rendering of faces with a 3d morphable model. IEEE Trans. Pattern Anal. Mach. Intell., 35(5):1080–1093,2013.

• Shu-Fan Wang and Shang-Hong Lai. Reconstructing 3d face model with associated expression deformation from a single face image via constructing alow-dimensional expression deformation manifold. IEEE Trans. Pattern Anal. Mach. Intell., 33(10):2115–2121, 2011.

• Ira Kemelmacher-Shlizerman and Ronen Basri. 3d face reconstruction from a single image using a single reference face shape. IEEE Trans. PatternAnal. Mach. Intell., 33(2):394–405, 2011.

Dataset

• UV dataset: J. Booth, A. Roussos, S. Zafeiriou, A. Ponniah, and D. Dunaway. A 3d morphable model learnt from 10,000 faces. In CVPR, 2016. 2, 7 (10,000 UV map) [Project]

• 3DMM model: Large Scale Face Model [Project][code]

• Expression Model: 4DFAB database - S. Cheng, I. Kotsia, M. Pantic, and S. Zafeiriou. 4dfab: a large scale 4d facial expression database for biometric applications. arXiv preprint arXiv:1712.01443, 2017. 7

• Test-set: MoFA test-set [Ayush Tewari, Michael Zollhöfer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Pérez, Christian Theobalt: MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction. ICCV 2017: 3735-3744][Paper]

• MICC Florence 3D Faces dataset (MICC)

Models

• Publicly available 3D shape and/or appearance models of human faces

models	public	geometry	appearance	data	comment	Project
Basel Face model (BFM)	2009	shape	per-vertex	200 individuals, each in neural expression	includes separate face parts	-
FaceWareHouse	2014	shape, expression	-	150 individuals, each with 20 expressions	-	-
Global and local model	2014	shape	-	99 individuals, each with 25 expressions	-	-