research-article

Neural Parameterization for Dynamic Human Head Editing

Authors:

Pedro V. SanderAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 6

Article No.: 236, Pages 1 - 15

https://doi.org/10.1145/3550454.3555494

Published: 30 November 2022 Publication History

Abstract

Implicit radiance functions emerged as a powerful scene representation for reconstructing and rendering photo-realistic views of a 3D scene. These representations, however, suffer from poor editability. On the other hand, explicit representations such as polygonal meshes allow easy editing but are not as suitable for reconstructing accurate details in dynamic human heads, such as fine facial features, hair, teeth, and eyes. In this work, we present Neural Parameterization (NeP), a hybrid representation that provides the advantages of both implicit and explicit methods. NeP is capable of photo-realistic rendering while allowing fine-grained editing of the scene geometry and appearance. We first disentangle the geometry and appearance by parameterizing the 3D geometry into 2D texture space. We enable geometric editability by introducing an explicit linear deformation blending layer. The deformation is controlled by a set of sparse key points, which can be explicitly and intuitively displaced to edit the geometry. For appearance, we develop a hybrid 2D texture consisting of an explicit texture map for easy editing and implicit view and time-dependent residuals to model temporal and view variations. We compare our method to several reconstruction and editing baselines. The results show that the NeP achieves almost the same level of rendering accuracy while maintaining high editability.

Supplemental Material

MP4 File

presentation

Download
314.12 MB

References

[1]

Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, and Ping Tan. 2020. Deep facial non-rigid multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5850--5860.

[2]

Linchao Bao, Xiangkai Lin, Yajing Chen, Haoxian Zhang, Sheng Wang, Xuefei Zhe, Di Kang, Haozhi Huang, Xinwei Jiang, Jue Wang, et al. 2020. High-Fidelity 3D Digital Human Head Creation from RGB-D Selfies. arXiv preprint arXiv:2010.05562 (2020).

[3]

Thabo Beeler, Fabian Hahn, Derek Bradley, Bernd Bickel, Paul Beardsley, Craig Gotsman, Robert W. Sumner, and Markus Gross. 2011. High-quality passive facial performance capture using anchor frames. ACM Trans. Graph. 30, Article 75 (August 2011), 10 pages. Issue 4.

Digital Library

[4]

Fausto Bernardini, Joshua Mittleman, Holly Rushmeier, Cláudio Silva, and Gabriel Taubin. 1999. The ball-pivoting algorithm for surface reconstruction. IEEE transactions on visualization and computer graphics 5, 4 (1999), 349--359.

Digital Library

[5]

Sai Bi, Stephen Lombardi, Shunsuke Saito, Tomas Simon, Shih-En Wei, Kevyn Mcphail, Ravi Ramamoorthi, Yaser Sheikh, and Jason Saragih. 2021. Deep relightable appearance models for animatable faces. ACM Transactions on Graphics (TOG) 40, 4 (2021), 1--15.

Digital Library

[6]

Sai Bi, Zexiang Xu, Pratul Srinivasan, Ben Mildenhall, Kalyan Sunkavalli, Miloš Hašan, Yannick Hold-Geoffroy, David Kriegman, and Ravi Ramamoorthi. 2020. Neural Reflectance Fields for Appearance Acquisition. arXiv:2008.03824 [cs.CV]

[7]

Volker Blanz and Thomas Vetter. 1999. A Morphable Model for the Synthesis of 3D Faces. In Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '99). ACM Press/Addison-Wesley Publishing Co., USA, 187--194.

Digital Library

[8]

Piotr Bojanowski, Armand Joulin, David Lopez-Paz, and Arthur Szlam. 2017. Optimizing the Latent Space of Generative Networks. arXiv:1707.05776 [stat.ML]

[9]

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

Digital Library

[10]

Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, and Hendrik P.A. Lensch. 2021. NeRD: Neural Reflectance Decomposition from Image Collections. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (Oct 2021).

[11]

Chen Cao, Vasu Agrawal, Fernando De La Torre, Lele Chen, Jason Saragih, Tomas Simon, and Yaser Sheikh. 2021. Real-time 3D neural facial animation from binocular video. ACM Transactions on Graphics (TOG) 40, 4 (2021), 1--17.

Digital Library

[12]

Edwin Catmull and James Clark. 1978. Recursively generated B-spline surfaces on arbitrary topological meshes. Computer-aided design 10, 6 (1978), 350--355.

[13]

Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein. 2021a. Efficient Geometry-aware 3D Generative Adversarial Networks. arXiv:2112.07945 [cs.CV]

[14]

Eric R. Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. 2021b. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Jun 2021).

[15]

Yue Chen, Xuan Wang, Qi Zhang, Xiaoyu Li, Xingyu Chen, Yu Guo, Jue Wang, and Fei Wang. 2022. UV Volumes for Real-time Rendering of Editable Free-view Human Performance. arXiv preprint arXiv:2203.14402 (2022).

[16]

Pei-Ze Chiang, Meng-Shiun Tsai, Hung-Yu Tseng, Wei-Sheng Lai, and Wei-Chen Chiu. 2022. Stylizing 3D Scene via Implicit Representation and HyperNetwork. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 1475--1484.

[17]

Forrester Cole, Kyle Genova, Avneesh Sud, Daniel Vlasic, and Zhoutong Zhang. 2021. Differentiable surface rendering via non-differentiable sampling. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 6088--6097.

[18]

Yu Deng, Jiaolong Yang, and Xin Tong. 2021. Deformed implicit field: Modeling 3d shapes with learned dense correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10286--10296.

[19]

Epic Games. 2018. CapturingReality. https://www.capturingreality.com

[20]

Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. 2021. Learning an animatable detailed 3D face model from in-the-wild images. ACM Transactions on Graphics (TOG) 40, 4 (2021), 1--13.

Digital Library

[21]

Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. 2018. Joint 3d face reconstruction and dense alignment with position map regression network. In Proceedings of the European conference on computer vision (ECCV). 534--551.

Digital Library

[22]

John Flynn, Michael Broxton, Paul Debevec, Matthew DuVall, Graham Fyffe, Ryan Overbeck, Noah Snavely, and Richard Tucker. 2019. Deepview: View synthesis with learned gradient descent. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2367--2376.

[23]

Graham Fyffe, Koki Nagano, Loc Huynh, Shunsuke Saito, Jay Busch, Andrew Jones, Hao Li, and Paul Debevec. 2017. Multi-View Stereo on Consistent Face Topology. In Computer Graphics Forum, Vol. 36. Wiley Online Library, 295--309.

[24]

Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. 2021. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8649--8658.

[25]

Baris Gecer, Stylianos Ploumpis, Irene Kotsia, and Stefanos Zafeiriou. 2019. Ganfit: Generative adversarial network fitting for high fidelity 3d face reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1155--1164.

[26]

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

[27]

Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Luthi, Sandro Schönborn, and Thomas Vetter. 2018. Morphable face models-an open framework. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). IEEE, 75--82.

Digital Library

[28]

Paulo Gotardo, Jérémy Riviere, Derek Bradley, Abhijeet Ghosh, and Thabo Beeler. 2018. Practical dynamic facial appearance modeling and acquisition. (2018).

[29]

Carsten Griwodz, Simone Gasparini, Lilian Calvet, Pierre Gurdjos, Fabien Castan, Benoit Maujean, Gregoire De Lillo, and Yann Lanthony. 2021. AliceVision Meshroom: An open-source 3D reconstruction pipeline. In Proceedings of the 12th ACM Multimedia Systems Conference - MMSys '21. ACM Press.

Digital Library

[30]

Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. 2021. Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis. arXiv preprint arXiv:2110.08985 (2021).

[31]

Jianzhu Guo, Xiangyu Zhu, Yang Yang, Fan Yang, Zhen Lei, and Stan Z Li. 2020. Towards fast, accurate and stable 3d dense face alignment. In European Conference on Computer Vision. Springer, 152--168.

Digital Library

[32]

Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, and Juyong Zhang. 2022. HeadNeRF: A Real-time NeRF-based Parametric Head Model. (2022).

[33]

Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang. 2018. DeepMVS: Learning Multi-View Stereopsis. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]

Abhishek Kar, Christian Häne, and Jitendra Malik. 2017. Learning a multi-view stereo machine. Advances in neural information processing systems 30 (2017).

[35]

Yoni Kasten, Dolev Ofri, Oliver Wang, and Tali Dekel. 2021. Layered neural atlases for consistent video editing. ACM Transactions on Graphics (TOG) 40, 6 (2021), 1--12.

Digital Library

[36]

Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada. 2018. Neural 3d mesh renderer. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3907--3916.

[37]

Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. 2006. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, Vol. 7.

Digital Library

[38]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs.LG]

[39]

Nicholas Kolkin, Jason Salavon, and Gregory Shakhnarovich. 2019. Style transfer by relaxed optimal transport and self-similarity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10051--10060.

[40]

Marc Levoy. 1990. Efficient ray tracing of volume data. ACM Transactions on Graphics (TOG) 9, 3 (1990), 245--261.

Digital Library

[41]

Jiaman Li, Zhengfei Kuang, Yajie Zhao, Mingming He, Karl Bladin, and Hao Li. 2020. Dynamic facial asset and rig generation from a single scan. ACM Trans. Graph. 39, 6 (2020), 215--1.

Digital Library

[42]

Moran Li, Haibin Huang, Yi Zheng, Mengtian Li, Nong Sang, and Chongyang Ma. 2021a. Implicit Neural Deformation for Multi-View Face Reconstruction. arXiv preprint arXiv:2112.02494 (2021).

[43]

Tianye Li, Timo Bolkart, Michael J Black, Hao Li, and Javier Romero. 2017. Learning a model of facial shape and expression from 4D scans. ACM Trans. Graph. 36, 6 (2017), 194--1.

Digital Library

[44]

Tianye Li, Shichen Liu, Timo Bolkart, Jiayi Liu, Hao Li, and Yajie Zhao. 2021b. Topologically Consistent Multi-View Face Inference Using Volumetric Sampling. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 3824--3834.

[45]

Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, and Zhaoyang Lv. 2021c. Neural 3d video synthesis. arXiv preprint arXiv:2103.02597 (2021).

[46]

Tzu-Mao Li, Miika Aittala, Frédo Durand, and Jaakko Lehtinen. 2018. Differentiable monte carlo ray tracing through edge sampling. ACM Transactions on Graphics (TOG) 37, 6 (2018), 1--11.

Digital Library

[47]

Shanchuan Lin, Linjie Yang, Imran Saleemi, and Soumyadip Sengupta. 2022. Robust High-Resolution Video Matting with Temporal Guidance. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 238--247.

[48]

Shichen Liu, Tianye Li, Weikai Chen, and Hao Li. 2019. Soft rasterizer: A differentiable renderer for image-based 3d reasoning. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 7708--7717.

[49]

Steven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang, Jun-Yan Zhu, and Bryan Russell. 2021. Editing conditional radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5773--5783.

[50]

Stephen Lombardi, Jason Saragih, Tomas Simon, and Yaser Sheikh. 2018. Deep appearance models for face rendering. ACM Transactions on Graphics (ToG) 37, 4 (2018), 1--13.

Digital Library

[51]

Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. 2019. Neural volumes: Learning dynamic renderable volumes from images. arXiv preprint arXiv:1906.07751 (2019).

[52]

Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, and Jason Saragih. 2021. Mixture of volumetric primitives for efficient neural rendering. ACM Transactions on Graphics (TOG) 40, 4 (2021), 1--13.

Digital Library

[53]

Matthew M Loper and Michael J Black. 2014. OpenDR: An approximate differentiable renderer. In European Conference on Computer Vision. Springer, 154--169.

[54]

Guillaume Loubet, Nicolas Holzschuch, and Wenzel Jakob. 2019. Reparameterizing discontinuous integrands for differentiable rendering. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1--14.

Digital Library

[55]

Fujun Luan, Shuang Zhao, Kavita Bala, and Zhao Dong. 2021. Unified shape and svbrdf recovery using differentiable monte carlo rendering. In Computer Graphics Forum, Vol. 40. Wiley Online Library, 101--113.

[56]

Keyang Luo, Tao Guan, Lili Ju, Haipeng Huang, and Yawei Luo. 2019. P-mvsnet: Learning patch-wise matching confidence aggregation for multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 10452--10461.

[57]

Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De La Torre, and Yaser Sheikh. 2021. Pixel codec avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 64--73.

[58]

Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4460--4470.

[59]

Ben Mildenhall, Pratul P Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar. 2019. Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM Transactions on Graphics (TOG) 38, 4 (2019), 1--14.

Digital Library

[60]

Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. 2020. Nerf: Representing scenes as neural radiance fields for view synthesis. In European conference on computer vision. Springer, 405--421.

Digital Library

[61]

Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. 2022. Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. arXiv preprint arXiv:2201.05989 (2022).

[62]

Michael Niemeyer and Andreas Geiger. 2021. Giraffe: Representing scenes as compositional generative neural feature fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11453--11464.

[63]

Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. 2020. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3504--3515.

[64]

Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. 2021a. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5865--5874.

[65]

Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-Brualla, and Steven M Seitz. 2021b. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. arXiv preprint arXiv:2106.13228 (2021).

[66]

Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. 2009. A 3D face model for pose and illumination invariant face recognition. In 2009 sixth IEEE international conference on advanced video and signal based surveillance. Ieee, 296--301.

[67]

Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. 2021. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10318--10327.

[68]

Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. 2021. H3d-net: Few-shot high-fidelity 3d head reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5620--5629.

[69]

Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. 2020. Accelerating 3d deep learning with pytorch3d. arXiv preprint arXiv:2007.08501 (2020).

[70]

Gernot Riegler and Vladlen Koltun. 2020a. Free View Synthesis. CoRR abs/2008.05511 (2020). arXiv:2008.05511 https://arxiv.org/abs/2008.05511

[71]

Gernot Riegler and Vladlen Koltun. 2020b. Stable View Synthesis. CoRR abs/2011.07233 (2020). arXiv:2011.07233 https://arxiv.org/abs/2011.07233

[72]

Johannes Lutz Schönberger and Jan-Michael Frahm. 2016. Structure-from-Motion Revisited. In Conference on Computer Vision and Pattern Recognition (CVPR).

[73]

Johannes Lutz Schönberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. 2016. Pixelwise View Selection for Unstructured Multi-View Stereo. In European Conference on Computer Vision (ECCV).

[74]

Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. 2020. Graf: Generative radiance fields for 3d-aware image synthesis. arXiv preprint arXiv:2007.02442 (2020).

[75]

Olga Sorkine and Marc Alexa. 2007. As-rigid-as-possible surface modeling. In Symposium on Geometry processing, Vol. 4. 109--116.

Digital Library

[76]

Olga Sorkine, Daniel Cohen-Or, Yaron Lipman, Marc Alexa, Christian Rössl, and H-P Seidel. 2004. Laplacian surface editing. In Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing. 175--184.

Digital Library

[77]

Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron. 2021. Nerv: Neural reflectance and visibility fields for relighting and view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7495--7504.

[78]

Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely. 2019. Pushing the boundaries of view extrapolation with multiplane images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 175--184.

[79]

Jingxiang Sun, Xuan Wang, Yichun Shi, Lizhen Wang, Jue Wang, and Yebin Liu. 2022. IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis. arXiv preprint arXiv:2205.15517 (2022).

[80]

Jingxiang Sun, Xuan Wang, Yong Zhang, Xiaoyu Li, Qi Zhang, Yebin Liu, and Jue Wang. 2021. FENeRF: Face Editing in Neural Radiance Fields. arXiv preprint arXiv:2111.15490 (2021).

[81]

Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. 2020. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems 33 (2020), 7537--7547.

[82]

Justus Thies, Michael Zollhöfer, and Matthias Nießner. 2019. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG) 38, 4 (2019), 1--12.

Digital Library

[83]

Can Wang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao. 2021a. CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields. arXiv preprint arXiv:2112.05139 (2021).

[84]

Lizhen Wang, Zhiyuan Chen, Tao Yu, Chenguang Ma, Liang Li, and Yebin Liu. 2022. FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset. arXiv preprint arXiv:2203.14057 (2022).

[85]

Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. 2021b. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689 (2021).

[86]

Xingkui Wei, Yinda Zhang, Zhuwen Li, Yanwei Fu, and Xiangyang Xue. 2020. Deepsfm: Structure from motion via deep bundle adjustment. In European conference on computer vision. Springer, 230--247.

Digital Library

[87]

Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. 2021. GAN Inversion: A Survey. arXiv:2101.05278 [cs.CV]

[88]

Wenqi Xian, Jia-Bin Huang, Johannes Kopf, and Changil Kim. 2021. Space-time neural irradiance fields for free-viewpoint video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9421--9431.

[89]

Fanbo Xiang, Zexiang Xu, Milos Hasan, Yannick Hold-Geoffroy, Kalyan Sunkavalli, and Hao Su. 2021. Neutex: Neural texture mapping for volumetric neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7119--7128.

[90]

Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. 2020a. Facescape: a large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 601--610.

[91]

Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. 2020b. FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[92]

Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. 2021. Volume rendering of neural implicit surfaces. Advances in Neural Information Processing Systems 34 (2021).

[93]

Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. 2020. Multiview neural surface reconstruction by disentangling geometry and appearance. Advances in Neural Information Processing Systems 33 (2020), 2492--2502.

[94]

Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. 2021. Plenoxels: Radiance Fields without Neural Networks. arXiv preprint arXiv:2112.05131 (2021).

[95]

Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, and Jing Liao. 2022a. FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing. arXiv preprint arXiv:2208.05751 (2022).

[96]

Longwen Zhang, Chuxiao Zeng, Qixuan Zhang, Hongyang Lin, Ruixiang Cao, Wei Yang, Lan Xu, and Jingyi Yu. 2022b. Video-driven Neural Physically-based Facial Asset for Production. arXiv preprint arXiv:2202.05592 (2022).

[97]

Chengwei Zheng and Feng Xu. 2021. DTexFusion: Dynamic Texture Fusion using a Consumer RGBD Sensor. IEEE Transactions on Visualization and Computer Graphics (2021).

[98]

Mingwu Zheng, Hongyu Yang, Di Huang, and Liming Chen. 2022. ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations. arXiv preprint arXiv:2203.14510 (2022).

[99]

Yufeng Zheng, Victoria Fernández Abrevaya, Xu Chen, Marcel C Bühler, Michael J Black, and Otmar Hilliges. 2021. IM Avatar: Implicit Morphable Head Avatars from Videos. arXiv preprint arXiv:2112.07471 (2021).

[100]

Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. 2018. Stereo magnification: Learning view synthesis using multiplane images. arXiv preprint arXiv:1805.09817 (2018).

Cited By

Fan JLv XZeng XBao ZHe ZGao M(2025)Unified Volumetric Avatar: Enabling flexible editing and rendering of neural human representationsImage and Vision Computing10.1016/j.imavis.2024.105345153(105345)Online publication date: Jan-2025
https://doi.org/10.1016/j.imavis.2024.105345
Xu BHu JHou FLin KWu WQian CHe Y(2024)Parameterization-Driven Neural Surface Reconstruction for Object-Oriented Editing in Neural RenderingComputer Vision – ECCV 202410.1007/978-3-031-72940-9_26(461-479)Online publication date: 17-Nov-2024
https://doi.org/10.1007/978-3-031-72940-9_26
Xu THu WLai YShan YZhang S(2024)Texture-GS: Disentangling the Geometry and Texture for 3D Gaussian Splatting EditingComputer Vision – ECCV 202410.1007/978-3-031-72698-9_3(37-53)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72698-9_3
Show More Cited By

Index Terms

Neural Parameterization for Dynamic Human Head Editing
1. Computing methodologies
  1. Computer graphics
    1. Rendering

Recommendations

LightFormer: Light-Oriented Global Neural Rendering in Dynamic Scene

The generation of global illumination in real time has been a long-standing challenge in the graphics community, particularly in dynamic scenes with complex illumination. Recent neural rendering techniques have shown great promise by utilizing neural ...
Relighting4D: Neural Relightable Human from Videos
Computer Vision – ECCV 2022
Abstract
Human relighting is a highly desirable yet challenging task. Existing works either require expensive one-light-at-a-time (OLAT) captured data using light stage or cannot freely change the viewpoints of the rendered body. In this work, we propose a ...
Deferred neural lighting: free-viewpoint relighting from unstructured photographs

We present deferred neural lighting, a novel method for free-viewpoint relighting from unstructured photographs of a scene captured with handheld devices. Our method leverages a scene-dependent neural rendering network for relighting a rough geometric ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 6

December 2022

1428 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3550454

Issue’s Table of Contents

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 November 2022

Published in TOG Volume 41, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

City University of Hong Kong

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
480
Total Downloads

Downloads (Last 12 months)100
Downloads (Last 6 weeks)7

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Fan JLv XZeng XBao ZHe ZGao M(2025)Unified Volumetric Avatar: Enabling flexible editing and rendering of neural human representationsImage and Vision Computing10.1016/j.imavis.2024.105345153(105345)Online publication date: Jan-2025
https://doi.org/10.1016/j.imavis.2024.105345
Xu BHu JHou FLin KWu WQian CHe Y(2024)Parameterization-Driven Neural Surface Reconstruction for Object-Oriented Editing in Neural RenderingComputer Vision – ECCV 202410.1007/978-3-031-72940-9_26(461-479)Online publication date: 17-Nov-2024
https://doi.org/10.1007/978-3-031-72940-9_26
Xu THu WLai YShan YZhang S(2024)Texture-GS: Disentangling the Geometry and Texture for 3D Gaussian Splatting EditingComputer Vision – ECCV 202410.1007/978-3-031-72698-9_3(37-53)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72698-9_3
Lin CNagano KKautz JChan EIqbal UGuibas LWetzstein GKhamis S(2023)Single-Shot Implicit Morphable Faces with Consistent Texture ParameterizationACM SIGGRAPH 2023 Conference Proceedings10.1145/3588432.3591494(1-12)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1145/3588432.3591494
Bai YFan YWang XZhang YSun JYuan CShan Y(2023)High-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00441(4541-4551)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.00441
Kumar SPeter KKingsly C(2023)De-noising and Demosaicking of Bayer image using deep convolutional attention residual learningMultimedia Tools and Applications10.1007/s11042-023-14334-z82:13(20323-20342)Online publication date: 26-Jan-2023
https://dl.acm.org/doi/10.1007/s11042-023-14334-z
Zhang JLi XWan ZWang CLiao J(2022)FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression EditingSIGGRAPH Asia 2022 Conference Papers10.1145/3550469.3555404(1-9)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1145/3550469.3555404

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents