{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:47:49Z","timestamp":1765356469500,"version":"3.41.0"},"reference-count":74,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2018,12,4]],"date-time":"2018-12-04T00:00:00Z","timestamp":1543881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2018,12,31]]},"abstract":"<jats:p>\n            Motivated by augmented and virtual reality applications such as telepresence, there has been a recent focus in\n            <jats:italic>real-time<\/jats:italic>\n            performance capture of humans under motion. However, given the real-time constraint, these systems often suffer from artifacts in geometry and texture such as holes and noise in the final rendering, poor lighting, and low-resolution textures. We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time. We call this approach\n            <jats:italic>neural (re-)rendering<\/jats:italic>\n            , and our live system \"LookinGood\". Our deep architecture is trained to produce high resolution and high quality images from a coarse rendering in real-time. First, we propose a self-supervised training method that does not require manual ground-truth annotation. We contribute a specialized reconstruction error that uses semantic information to focus on relevant parts of the subject, e.g. the face. We also introduce a\n            <jats:italic>salient reweighing scheme<\/jats:italic>\n            of the loss function that is able to discard outliers. We specifically design the system for virtual and augmented reality headsets where the consistency between the left and right eye plays a crucial role in the final user experience. Finally, we generate temporally stable results by explicitly minimizing the difference between two consecutive frames. We tested the proposed system in two different scenarios: one involving a single RGB-D sensor, and upper body reconstruction of an actor, the second consisting of full body 360\u00b0 capture. Through extensive experimentation, we demonstrate how our system generalizes across unseen sequences and subjects.\n          <\/jats:p>","DOI":"10.1145\/3272127.3275099","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T19:16:10Z","timestamp":1543432570000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":85,"title":["<i>LookinGood<\/i>"],"prefix":"10.1145","volume":"37","author":[{"given":"Ricardo","family":"Martin-Brualla","sequence":"first","affiliation":[{"name":"Google Inc."}]},{"given":"Rohit","family":"Pandey","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Shuoran","family":"Yang","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Pavel","family":"Pidlypenskyi","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Jonathan","family":"Taylor","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Julien","family":"Valentin","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Sameh","family":"Khamis","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Philip","family":"Davidson","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Anastasia","family":"Tkach","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Peter","family":"Lincoln","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Adarsh","family":"Kowdle","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Christoph","family":"Rhemann","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Dan B","family":"Goldman","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Cem","family":"Keskin","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Steve","family":"Seitz","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Shahram","family":"Izadi","sequence":"additional","affiliation":[{"name":"Google Inc."}]},{"given":"Sean","family":"Fanello","sequence":"additional","affiliation":[{"name":"Google Inc."}]}],"member":"320","published-online":{"date-parts":[[2018,12,4]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980257"},{"key":"e_1_2_2_2_1","first-page":"1","article-title":"PatchMatch Stereo-Stereo Matching with Slanted Support Windows","volume":"11","author":"Bleyer Michael","year":"2011","unstructured":"Michael Bleyer , Christoph Rhemann , and Carsten Rother . 2011 . PatchMatch Stereo-Stereo Matching with Slanted Support Windows .. In Bmvc , Vol. 11. 1 -- 11 . Michael Bleyer, Christoph Rhemann, and Carsten Rother. 2011. PatchMatch Stereo-Stereo Matching with Slanted Support Windows.. In Bmvc, Vol. 11. 1--11.","journal-title":"Bmvc"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1201775.882309"},{"key":"e_1_2_2_4_1","volume-title":"4D Video Textures for Interactive Character Appearance. EUROGRAPHICS","author":"Casas Dan","year":"2014","unstructured":"Dan Casas , Marco Volino , John Collomosse , and Adrian Hilton . 2014. 4D Video Textures for Interactive Character Appearance. EUROGRAPHICS ( 2014 ). Dan Casas, Marco Volino, John Collomosse, and Adrian Hilton. 2014. 4D Video Textures for Interactive Character Appearance. EUROGRAPHICS (2014)."},{"key":"e_1_2_2_5_1","volume-title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. CoRR abs\/1802.02611","author":"Chen Liang-Chieh","year":"2018","unstructured":"Liang-Chieh Chen , Yukun Zhu , George Papandreou , Florian Schroff , and Hartwig Adam . 2018. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. CoRR abs\/1802.02611 ( 2018 ). Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. 2018. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. CoRR abs\/1802.02611 (2018)."},{"key":"e_1_2_2_6_1","volume-title":"Photographic Image Synthesis with Cascaded Refinement Networks. ICCV","author":"Chen Qifeng","year":"2017","unstructured":"Qifeng Chen and Vladlen Koltun . 2017. Photographic Image Synthesis with Cascaded Refinement Networks. ICCV ( 2017 ). Qifeng Chen and Vladlen Koltun. 2017. Photographic Image Synthesis with Cascaded Refinement Networks. ICCV (2017)."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766945"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/237170.237269"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.693"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12544"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/344779.344855"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/237170.237191"},{"key":"e_1_2_2_13_1","doi-asserted-by":"crossref","unstructured":"J. Deng W. Dong R. Socher L.-J. Li K. Li and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.  J. Deng W. Dong R. Socher L.-J. Li K. Li and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_2_14_1","doi-asserted-by":"crossref","unstructured":"A. Dosovitskiy J. T. Springenberg M. Tatarchenko and T. Brox. 2015. Learning to Generate Chairs with Convolutional Networks. CVPR (2015).  A. Dosovitskiy J. T. Springenberg M. Tatarchenko and T. Brox. 2015. Learning to Generate Chairs with Convolutional Networks. CVPR (2015).","DOI":"10.1109\/CVPR.2015.7298761"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3130800.3130801"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925969"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3190834.3190843"},{"key":"e_1_2_2_18_1","doi-asserted-by":"crossref","unstructured":"M. Eisemann B. De Decker M. Magnor P. Bekaert E. De Aguiar N. Ahmed C. Theobalt and A. Sellent. 2008. Floating Textures. Computer Graphics Forum (2008).  M. Eisemann B. De Decker M. Magnor P. Bekaert E. De Aguiar N. Ahmed C. Theobalt and A. Sellent. 2008. Floating Textures. Computer Graphics Forum (2008).","DOI":"10.1111\/j.1467-8659.2008.01138.x"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2856400.2876015"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.221"},{"key":"e_1_2_2_21_1","doi-asserted-by":"crossref","unstructured":"S. R. Fanello C. Rhemann V. Tankovich A. Kowdle S. Orts Escolano D. Kim and S. Izadi. 2016. HyperDepth: Learning Depth from Structured Light Without Matching. In CVPR.  S. R. Fanello C. Rhemann V. Tankovich A. Kowdle S. Orts Escolano D. Kim and S. Izadi. 2016. HyperDepth: Learning Depth from Structured Light Without Matching. In CVPR.","DOI":"10.1109\/CVPR.2016.587"},{"key":"e_1_2_2_22_1","volume-title":"Philip Davidson, and Shahram Izadi.","author":"Fanello Sean Ryan","year":"2017","unstructured":"Sean Ryan Fanello , Julien Valentin , Adarsh Kowdle , Christoph Rhemann , Vladimir Tankovich , Carlo Ciliberto , Philip Davidson, and Shahram Izadi. 2017 a. Low Compute and Fully Parallel Computer Vision with HashMatch. In ICCV. Sean Ryan Fanello, Julien Valentin, Adarsh Kowdle, Christoph Rhemann, Vladimir Tankovich, Carlo Ciliberto, Philip Davidson, and Shahram Izadi. 2017a. Low Compute and Fully Parallel Computer Vision with HashMatch. In ICCV."},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.692"},{"key":"e_1_2_2_24_1","volume-title":"Deep Stereo: Learning to Predict New Views from the World's Imagery. In CVPR.","author":"Flynn J.","year":"2016","unstructured":"J. Flynn , I. Neulander , J. Philbin , and N. Snavely . 2016 . Deep Stereo: Learning to Predict New Views from the World's Imagery. In CVPR. J. Flynn, I. Neulander, J. Philbin, and N. Snavely. 2016. Deep Stereo: Learning to Predict New Views from the World's Imagery. In CVPR."},{"volume-title":"IEEE International Conference on Computational Photography.","author":"Fyffe G.","key":"e_1_2_2_25_1","unstructured":"G. Fyffe and P. Debevec . 2015. Single-Shot Reflectance Measurement from Polarized Color Gradient Illumination . In IEEE International Conference on Computational Photography. G. Fyffe and P. Debevec. 2015. Single-Shot Reflectance Measurement from Polarized Color Gradient Illumination. In IEEE International Conference on Computational Photography."},{"key":"e_1_2_2_26_1","volume-title":"Per Christian Hansen, and Dianne P. O'Leary","author":"Golub Gene H.","year":"1999","unstructured":"Gene H. Golub , Per Christian Hansen, and Dianne P. O'Leary . 1999 . Tikhonov Regularization and Total Least Squares. SIAM ( 1999). Gene H. Golub, Per Christian Hansen, and Dianne P. O'Leary. 1999. Tikhonov Regularization and Total Least Squares. SIAM (1999)."},{"key":"e_1_2_2_27_1","unstructured":"Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NIPS.   Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NIPS."},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/237170.237200"},{"volume-title":"High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference. In IEEE International Conference on Computer Vision (ICCV).","author":"Han X.","key":"e_1_2_2_29_1","unstructured":"X. Han , Z. Li , H. Huang , E. Kalogerakis , and Y. Yu . 2017 . High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference. In IEEE International Conference on Computer Vision (ICCV). X. Han, Z. Li, H. Huang, E. Kalogerakis, and Y. Yu. 2017. High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference. In IEEE International Conference on Computer Vision (ICCV)."},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_22"},{"key":"e_1_2_2_31_1","unstructured":"Intel. 2016. freeD technology.  Intel. 2016. freeD technology."},{"key":"e_1_2_2_32_1","volume-title":"Image-to-Image Translation with Conditional Adversarial Networks. arxiv","author":"Isola Phillip","year":"2016","unstructured":"Phillip Isola , Jun-Yan Zhu , Tinghui Zhou , and Alexei A Efros . 2016. Image-to-Image Translation with Conditional Adversarial Networks. arxiv ( 2016 ). Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2016. Image-to-Image Translation with Conditional Adversarial Networks. arxiv (2016)."},{"key":"e_1_2_2_33_1","unstructured":"Max Jaderberg Karen Simonyan Andrew Zisserman and Koray Kavukcuoglu. 2015. Spatial Transformer Networks. In NIPS.   Max Jaderberg Karen Simonyan Andrew Zisserman and Koray Kavukcuoglu. 2015. Spatial Transformer Networks. In NIPS."},{"key":"e_1_2_2_34_1","unstructured":"L. C. Jain and L. R. Medsker. 1999. Recurrent Neural Networks: Design and Applications. CRC Press.   L. C. Jain and L. R. Medsker. 1999. Recurrent Neural Networks: Design and Applications. CRC Press."},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33786-4_9"},{"key":"e_1_2_2_36_1","volume-title":"Deep View Morphing. CoRR","author":"Ji Dinghuang","year":"2017","unstructured":"Dinghuang Ji , Junghyun Kwon , Max McFarland , and Silvio Savarese . 2017. Deep View Morphing. CoRR ( 2017 ). Dinghuang Ji, Junghyun Kwon, Max McFarland, and Silvio Savarese. 2017. Deep View Morphing. CoRR (2017)."},{"key":"e_1_2_2_37_1","volume-title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution. CoRR","author":"Johnson Justin","year":"2016","unstructured":"Justin Johnson , Alexandre Alahi , and Fei-Fei Li. 2016. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. CoRR ( 2016 ). Justin Johnson, Alexandre Alahi, and Fei-Fei Li. 2016. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. CoRR (2016)."},{"key":"e_1_2_2_38_1","volume-title":"Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies. CVPR","author":"Joo Hanbyul","year":"2018","unstructured":"Hanbyul Joo , Tomas Simon , and Yaser Sheikh . 2018 . Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies. CVPR (2018). Hanbyul Joo, Tomas Simon, and Yaser Sheikh. 2018. Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies. CVPR (2018)."},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487228.2487237"},{"key":"e_1_2_2_40_1","volume-title":"Christoph Rhemann, Julien Valentin, Adarsh Kowdle, and Shahram Izadi.","author":"Khamis Sameh","year":"2018","unstructured":"Sameh Khamis , Sean Ryan Fanello , Christoph Rhemann, Julien Valentin, Adarsh Kowdle, and Shahram Izadi. 2018 . StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction . ECCV (2018). Sameh Khamis, Sean Ryan Fanello, Christoph Rhemann, Julien Valentin, Adarsh Kowdle, and Shahram Izadi. 2018. StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction. ECCV (2018)."},{"key":"e_1_2_2_41_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba . 2014 . Adam : A Method for Stochastic Optimization. CoRR ( 2014). Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. CoRR (2014)."},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275062"},{"key":"e_1_2_2_43_1","unstructured":"Philipp Kr\u00e4henb\u00fchl and Vladlen Koltun. 2011. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. In NIPS.   Philipp Kr\u00e4henb\u00fchl and Vladlen Koltun. 2011. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. In NIPS."},{"key":"e_1_2_2_44_1","volume-title":"Tenenbaum","author":"Kulkarni Tejas D.","year":"2015","unstructured":"Tejas D. Kulkarni , William F. Whitney , Pushmeet Kohli , and Joshua B . Tenenbaum . 2015 . Deep Convolutional Inverse Graphics Network. In NIPS. Tejas D. Kulkarni, William F. Whitney, Pushmeet Kohli, and Joshua B. Tenenbaum. 2015. Deep Convolutional Inverse Graphics Network. In NIPS."},{"key":"e_1_2_2_45_1","doi-asserted-by":"crossref","unstructured":"V. Lempitsky and D. Ivanov. 2007. Seamless Mosaicing of Image-Based Texture Maps. In CVPR.  V. Lempitsky and D. Ivanov. 2007. Seamless Mosaicing of Image-Based Texture Maps. In CVPR.","DOI":"10.1109\/CVPR.2007.383078"},{"key":"e_1_2_2_46_1","volume-title":"Focal Loss for Dense Object Detection. CoRR","author":"Lin Tsung-Yi","year":"2017","unstructured":"Tsung-Yi Lin , Priya Goyal , Ross B. Girshick , Kaiming He , and Piotr Doll\u00e1r . 2017. Focal Loss for Dense Object Detection. CoRR ( 2017 ). Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Doll\u00e1r. 2017. Focal Loss for Dense Object Detection. CoRR (2017)."},{"key":"e_1_2_2_47_1","doi-asserted-by":"crossref","unstructured":"R. A. Newcombe D. Fox and S. M. Seitz. 2015. DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In CVPR.  R. A. Newcombe D. Fox and S. M. Seitz. 2015. DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In CVPR.","DOI":"10.1109\/CVPR.2015.7298631"},{"key":"e_1_2_2_48_1","volume-title":"ESPReSSo: Efficient Slanted PatchMatch for Real-time Spacetime Stereo. 3DV","author":"Nover Harris","year":"2018","unstructured":"Harris Nover , Supreeth Achar , and Dan B Goldman . 2018. ESPReSSo: Efficient Slanted PatchMatch for Real-time Spacetime Stereo. 3DV ( 2018 ). Harris Nover, Supreeth Achar, and Dan B Goldman. 2018. ESPReSSo: Efficient Slanted PatchMatch for Real-time Spacetime Stereo. 3DV (2018)."},{"key":"e_1_2_2_49_1","volume-title":"Deconvolution and Checkerboard Artifacts. Distill","author":"Odena Augustus","year":"2016","unstructured":"Augustus Odena , Vincent Dumoulin , and Chris Olah . 2016. Deconvolution and Checkerboard Artifacts. Distill ( 2016 ). Augustus Odena, Vincent Dumoulin, and Chris Olah. 2016. Deconvolution and Checkerboard Artifacts. Distill (2016)."},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2984511.2984517"},{"key":"e_1_2_2_51_1","doi-asserted-by":"crossref","unstructured":"E. Park J. Yang E. Yumer D. Ceylan and A. C. Berg. 2017. Transformation-Grounded Image Generation Network for Novel 3D View Synthesis. In CVPR.  E. Park J. Yang E. Yumer D. Ceylan and A. C. Berg. 2017. Transformation-Grounded Image Generation Network for Novel 3D View Synthesis. In CVPR.","DOI":"10.1109\/CVPR.2017.82"},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073679"},{"key":"e_1_2_2_53_1","volume-title":"Point-ProNets: Consolidation of Point Clouds with Convolutional Neural Networks. Computer Graphics Forum","author":"Riccardo Roveri","year":"2018","unstructured":"Roveri Riccardo , Oztireli A. Cengiz , Pandele Ioana , and Gross Markus . 2018. Point-ProNets: Consolidation of Point Clouds with Convolutional Neural Networks. Computer Graphics Forum ( 2018 ). Roveri Riccardo, Oztireli A. Cengiz, Pandele Ioana, and Gross Markus. 2018. Point-ProNets: Consolidation of Point Clouds with Convolutional Neural Networks. Computer Graphics Forum (2018)."},{"key":"e_1_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.166"},{"key":"e_1_2_2_55_1","doi-asserted-by":"crossref","unstructured":"Gernot Riegler Ren\u00c3l' Ranftl Matthias R\u00c3ijther Thomas Pock and Horst Bischof. 2015. Depth Restoration via Joint Training of a Global Regression Model and CNNs. In BMVC.  Gernot Riegler Ren\u00c3l' Ranftl Matthias R\u00c3ijther Thomas Pock and Horst Bischof. 2015. Depth Restoration via Joint Training of a Global Regression Model and CNNs. In BMVC.","DOI":"10.5244\/C.29.58"},{"key":"e_1_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.701"},{"key":"e_1_2_2_57_1","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger , Philipp Fischer , and Thomas Brox . 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI ( 2015 ). Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI (2015)."},{"key":"e_1_2_2_58_1","doi-asserted-by":"crossref","unstructured":"S. Schulter C. Leistner and H. Bischof. 2015. Fast and accurate image upscaling with super-resolution forests. In CVPR.  S. Schulter C. Leistner and H. Bischof. 2015. Fast and accurate image upscaling with super-resolution forests. In CVPR.","DOI":"10.1109\/CVPR.2015.7299003"},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2013.12"},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073640"},{"key":"e_1_2_2_61_1","volume-title":"Adarsh Kowdle, Christoph Rhemann, Max Dzitsiuk, Mirko Schmidt, Julien Valentin, and Shahram Izadi.","author":"Tankovich Vladimir","year":"2018","unstructured":"Vladimir Tankovich , Michael Schoenberg , Sean Ryan Fanello , Adarsh Kowdle, Christoph Rhemann, Max Dzitsiuk, Mirko Schmidt, Julien Valentin, and Shahram Izadi. 2018 . SOS : Stereo Matching in O(1) with Slanted Support Windows. IROS ( 2018). Vladimir Tankovich, Michael Schoenberg, Sean Ryan Fanello, Adarsh Kowdle, Christoph Rhemann, Max Dzitsiuk, Mirko Schmidt, Julien Valentin, and Shahram Izadi. 2018. SOS: Stereo Matching in O(1) with Slanted Support Windows. IROS (2018)."},{"key":"e_1_2_2_62_1","volume-title":"Multi-view 3d models from single images with a convolutional network. ECCV","author":"Tatarchenko Maxim","year":"2016","unstructured":"Maxim Tatarchenko , Alexey Dosovitskiy , and Thomas Brox . 2016. Multi-view 3d models from single images with a convolutional network. ECCV ( 2016 ). Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox. 2016. Multi-view 3d models from single images with a convolutional network. ECCV (2016)."},{"key":"e_1_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2929464.2929475"},{"key":"e_1_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/2993369.2993375"},{"key":"e_1_2_2_65_1","doi-asserted-by":"crossref","unstructured":"Marco Volino Dan Casas John Collomosse and Adrian Hilton. 2014. Optimal Representation of Multiple View Video. In BMVC.  Marco Volino Dan Casas John Collomosse and Adrian Hilton. 2014. Optimal Representation of Multiple View Video. In BMVC.","DOI":"10.5244\/C.28.8"},{"key":"e_1_2_2_66_1","volume-title":"Christoph Rhemann, Shahram Izadi, and Pushmeet Kohli.","author":"Wang Shenlong","year":"2016","unstructured":"Shenlong Wang , Sean Ryan Fanello , Christoph Rhemann, Shahram Izadi, and Pushmeet Kohli. 2016 . The Global Patch Collider. CVPR ( 2016). Shenlong Wang, Sean Ryan Fanello, Christoph Rhemann, Shahram Izadi, and Pushmeet Kohli. 2016. The Global Patch Collider. CVPR (2016)."},{"key":"e_1_2_2_67_1","unstructured":"Jimei Yang Scott Reed Ming-Hsuan Yang and Honglak Lee. 2015. Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis. In NIPS.   Jimei Yang Scott Reed Ming-Hsuan Yang and Honglak Lee. 2015. Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis. In NIPS."},{"key":"e_1_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.104"},{"key":"e_1_2_2_69_1","volume-title":"ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems. ECCV","author":"Zhang Yinda","year":"2018","unstructured":"Yinda Zhang , Sameh Khamis , Christoph Rhemann , Julien Valentin , Adarsh Kowdle , Vladimir Tankovich , Michael Schoenberg , Shahram Izadi , Thomas Funkhouser , and Sean Fanello . 2018. ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems. ECCV ( 2018 ). Yinda Zhang, Sameh Khamis, Christoph Rhemann, Julien Valentin, Adarsh Kowdle, Vladimir Tankovich, Michael Schoenberg, Shahram Izadi, Thomas Funkhouser, and Sean Fanello. 2018. ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems. ECCV (2018)."},{"key":"e_1_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/1073204.1073325"},{"key":"e_1_2_2_71_1","volume-title":"Efros","author":"Zhou Tinghui","year":"2016","unstructured":"Tinghui Zhou , Shubham Tulsiani , Weilun Sun , Jitendra Malik , and Alexei A . Efros . 2016 . View Synthesis by Appearance Flow. CoRR ( 2016). Tinghui Zhou, Shubham Tulsiani, Weilun Sun, Jitendra Malik, and Alexei A. Efros. 2016. View Synthesis by Appearance Flow. CoRR (2016)."},{"key":"e_1_2_2_72_1","unstructured":"Jun-Yan Zhu Taesung Park Phillip Isola and Alexei A Efros. 2017. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In ICCV.  Jun-Yan Zhu Taesung Park Phillip Isola and Alexei A Efros. 2017. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In ICCV."},{"key":"e_1_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015766"},{"key":"e_1_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601165"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3272127.3275099","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3272127.3275099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:44:26Z","timestamp":1750207466000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3272127.3275099"}},"subtitle":["enhancing performance capture with real-time neural re-rendering"],"short-title":[],"issued":{"date-parts":[[2018,12,4]]},"references-count":74,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2018,12,31]]}},"alternative-id":["10.1145\/3272127.3275099"],"URL":"https:\/\/doi.org\/10.1145\/3272127.3275099","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"type":"print","value":"0730-0301"},{"type":"electronic","value":"1557-7368"}],"subject":[],"published":{"date-parts":[[2018,12,4]]},"assertion":[{"value":"2018-12-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}