Instant Multi-View Head Capture through Learnable Registration
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[1] P. Maragos,et al. Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos , 2022, ArXiv.
[2] Michael J. Black,et al. Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation , 2022, ECCV.
[3] Michael J. Black,et al. EMOCA: Emotion Driven Monocular Face Capture and Animation , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Wojciech Zielonka,et al. Towards Metrical Reconstruction of Human Faces , 2022, ECCV.
[5] V. Sharmanska,et al. DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Stephan J. Garbin,et al. 3D face reconstruction with dense landmarks , 2022, ECCV.
[7] Di Huang,et al. ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Bing Liu,et al. Multi-view stereo in the Deep Learning Era: A comprehensive revfiew , 2021, Displays.
[9] Feng Liu,et al. Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation , 2020, International Journal of Computer Vision.
[10] Michael J. Black,et al. Learning an animatable detailed 3D face model from in-the-wild images , 2020, ACM Trans. Graph..
[11] Tao Yu,et al. Deep Implicit Templates for 3D Shape Representation , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Gemma Piella,et al. Survey on 3D face reconstruction from uncalibrated images , 2020, Comput. Sci. Rev..
[13] Feng Liu,et al. Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence , 2020, NeurIPS.
[14] Long Quan,et al. Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency , 2020, ECCV.
[15] Thabo Beeler,et al. Single-shot high-quality facial geometry and skin appearance capture , 2020, ACM Trans. Graph..
[16] Zhaopeng Cui,et al. Deep Facial Non-Rigid Multi-View Stereo , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Ruigang Yang,et al. FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Siyu Zhu,et al. Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[20] Eimear O' Sullivan,et al. Towards a Complete 3D Morphable Model of the Human Head , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] Sanja Fidler,et al. Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research , 2019, ArXiv.
[22] Nick Pears,et al. Statistical Modeling of Craniofacial Shape and Texture , 2019, International Journal of Computer Vision.
[23] Xavier Giró-i-Nieto,et al. Multi-View 3D Face Reconstruction in the Wild Using Siamese Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[24] T. Vetter,et al. 3D Morphable Face Models—Past, Present, and Future , 2019, ACM Trans. Graph..
[25] Michael J. Black,et al. Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Victor Lempitsky,et al. Learnable Triangulation of Human Pose , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[27] Stephen Lin,et al. DPSNet: End-to-end Deep Plane Sweep Stereo , 2019, ICLR.
[28] Yichen Wei,et al. 3D Dense Face Alignment via Graph Convolution Networks , 2019, ArXiv.
[29] King Ngi Ngan,et al. MVF-Net: Multi-View 3D Face Morphable Model Regression , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Jiaolong Yang,et al. Accurate 3D Face Reconstruction With Weakly-Supervised Learning: From Single Image to Image Set , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[31] Stefanos Zafeiriou,et al. GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Feng Liu,et al. 3D Face Modeling From Diverse Raw Scan Data , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[33] Hans-Peter Seidel,et al. FML: Face Model Learning From Videos , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Yi Zhou,et al. On the Continuity of Rotation Representations in Neural Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Yaser Sheikh,et al. Deep incremental learning for efficient high-fidelity face tracking , 2018, ACM Trans. Graph..
[36] Gordon Wetzstein,et al. DeepVoxels: Learning Persistent 3D Feature Embeddings , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Edmond Boyer,et al. Spatiotemporal Modeling for Efficient Registration of Dynamic 3D Faces , 2018, 2018 International Conference on 3D Vision (3DV).
[38] William T. Freeman,et al. Unsupervised Training for 3D Morphable Model Regression , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[39] Patrick Pérez,et al. State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications , 2018, Comput. Graph. Forum.
[40] Long Quan,et al. MVSNet: Depth Inference for Unstructured Multi-view Stereo , 2018, ECCV.
[41] Xi Zhou,et al. Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network , 2018, ECCV.
[42] M. Zollhöfer,et al. Self-Supervised Multi-level Face Model Learning for Monocular Reconstruction at Over 250 Hz , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[43] Michael J. Black,et al. Learning a model of facial shape and expression from 4D scans , 2017, ACM Trans. Graph..
[44] Frank Hutter,et al. Decoupled Weight Decay Regularization , 2017, ICLR.
[45] William Smith,et al. A 3D Morphable Model of Craniofacial Shape and Texture Variation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[46] Ioannis A. Kakadiaris,et al. Multi-view 3D face reconstruction with deep recurrent neural networks , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).
[47] William A. P. Smith,et al. What Does 2D Geometric Information Really Tell Us About 3D Face Shape? , 2017, International Journal of Computer Vision.
[48] Jitendra Malik,et al. Learning a Multi-View Stereo Machine , 2017, NIPS.
[49] Mike Seymour,et al. Meet Mike: epic avatars , 2017, SIGGRAPH VR Village.
[50] Andrew Jones,et al. Multi‐View Stereo on Consistent Face Topology , 2017, Comput. Graph. Forum.
[51] Ioannis A. Kakadiaris,et al. End-to-End 3D Face Reconstruction with Deep Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[52] Justus Thies,et al. InverseFaceNet: Deep Monocular Inverse Face Rendering , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[53] Patrick Pérez,et al. MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[54] Georgios Tzimiropoulos,et al. Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[55] Tal Hassner,et al. Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[56] Iasonas Kokkinos,et al. DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Matan Sela,et al. 3D Face Reconstruction by Learning from Synthetic Data , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[58] Justus Thies,et al. Face2Face: Real-Time Face Capture and Reenactment of RGB Videos , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Chao Zhang,et al. Functional Faces: Groupwise Dense Correspondence Using Functional Maps , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[60] Stefanos Zafeiriou,et al. A 3D Morphable Model Learnt from 10,000 Faces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[61] Christian Theobalt,et al. Reconstruction of Personalized 3D Face Rigs from Monocular Video , 2016, ACM Trans. Graph..
[62] William A. P. Smith,et al. Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences , 2016, ACCV Workshops.
[63] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[64] Timo Bolkart,et al. A Groupwise Multilinear Correspondence Optimization for 3D Faces , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[65] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[66] Yiying Tong,et al. Unconstrained 3D face reconstruction , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[67] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[68] Ira Kemelmacher-Shlizerman,et al. Total Moving Face Reconstruction , 2014, ECCV.
[69] Yiying Tong,et al. FaceWarehouse: A 3D Facial Expression Database for Visual Computing , 2014, IEEE Transactions on Visualization and Computer Graphics.
[70] Ira Kemelmacher-Shlizerman,et al. Internet Based Morphable Model , 2013, 2013 IEEE International Conference on Computer Vision.
[71] Oswald Aldrian,et al. Inverse Rendering of Faces with a 3D Morphable Model , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[72] F. Prieto,et al. Fully automatic expression-invariant face correspondence , 2012, Machine Vision and Applications.
[73] Paul E. Debevec,et al. Multiview face capture using polarized spherical gradient illumination , 2011, ACM Trans. Graph..
[74] Ira Kemelmacher-Shlizerman,et al. Face reconstruction in the wild , 2011, 2011 International Conference on Computer Vision.
[75] Ioannis A. Kakadiaris,et al. Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[76] Derek Bradley,et al. High-quality passive facial performance capture using anchor frames , 2011, ACM Trans. Graph..
[77] W. Heidrich,et al. High resolution passive facial performance capture , 2010, ACM Trans. Graph..
[78] Leonidas J. Guibas,et al. Robust single-view geometry and motion reconstruction , 2009, ACM Trans. Graph..
[79] Paul Debevec,et al. The Digital Emily project: photoreal facial modeling and animation , 2009, SIGGRAPH '09.
[80] Thomas Vetter,et al. Expression invariant 3D face recognition with a Morphable Model , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[81] Pieter Peers,et al. Rapid Acquisition of Specular and Diffuse Normal Maps from Polarized Spherical Gradient Illumination , 2007 .
[82] Paul J. Besl,et al. A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[83] Xinguo Liu,et al. Light-Weight Multi-view Topology Consistent Facial Geometry and Reflectance Capture , 2021, CGI.
[84] Matthew Turk,et al. A Morphable Model For The Synthesis Of 3D Faces , 1999, SIGGRAPH.
[85] Stuart Geman,et al. Statistical methods for tomographic image reconstruction , 1987 .