{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:15:10Z","timestamp":1775582110983,"version":"3.50.1"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030586034","type":"print"},{"value":"9783030586041","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58604-1_14","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T22:02:49Z","timestamp":1604354569000},"page":"220-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images"],"prefix":"10.1007","author":[{"given":"Stephan J.","family":"Garbin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marek","family":"Kowalski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jamie","family":"Shotton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN++: How to edit the embedded images? (2019)","DOI":"10.1109\/CVPR42600.2020.00832"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN: How to embed images into the StyleGAN latent space? CoRR abs\/1904.03189 (2019). http:\/\/arxiv.org\/abs\/1904.03189","DOI":"10.1109\/ICCV.2019.00453"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"AlBahar, B., Huang, J.B.: Guided image-to-image translation with bi-directional feature transformation (2019)","DOI":"10.1109\/ICCV.2019.00911"},{"key":"14_CR4","doi-asserted-by":"publisher","unstructured":"Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.: OpenFace 2.0: facial behavior analysis toolkit. In: 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pp. 59\u201366, May 2018. https:\/\/doi.org\/10.1109\/FG.2018.00019","DOI":"10.1109\/FG.2018.00019"},{"key":"14_CR5","unstructured":"Baltrusaitis, T., et al.: A high fidelity synthetic face framework for computer vision. Technical Report MSR-TR-2020-24, Microsoft (July 2020). https:\/\/www.microsoft.com\/en-us\/research\/publication\/high-fidelity-face-synthetics\/"},{"key":"14_CR6","unstructured":"Benaim, S., Wolf, L.: One-shot unsupervised cross domain translation. CoRR abs\/1806.06029 (2018). http:\/\/arxiv.org\/abs\/1806.06029"},{"issue":"4","key":"14_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2897824.2925962","volume":"35","author":"P B\u00e9rard","year":"2016","unstructured":"B\u00e9rard, P., Bradley, D., Gross, M., Beeler, T.: Lightweight eye capture using a parametric model. ACM Trans. Graph. 35(4), 1\u201312 (2016). https:\/\/doi.org\/10.1145\/2897824.2925962","journal-title":"ACM Trans. Graph."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Bi, S., Sunkavalli, K., Perazzi, F., Shechtman, E., Kim, V.G., Ramamoorthi, R.: Deep CG2Real: synthetic-to-real translation via image disentanglement. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.00282"},{"key":"14_CR9","unstructured":"Bishop, C.M.: Mixture density networks. Technical report, Citeseer (1994)"},{"issue":"1","key":"14_CR10","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/0146-664X(81)90092-7","volume":"16","author":"PJ Burt","year":"1981","unstructured":"Burt, P.J.: Fast filter transform for image processing. Comput. Graph. Image Proc. 16(1), 20\u201351 (1981). https:\/\/doi.org\/10.1016\/0146-664X(81)90092-7","journal-title":"Comput. Graph. Image Proc."},{"key":"14_CR11","doi-asserted-by":"publisher","unstructured":"Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. In: Fischler, M.A., Firschein, O. (eds.) Readings in Computer Vision, pp. 671\u2013679. Morgan Kaufmann, San Francisco (1987). https:\/\/doi.org\/10.1016\/B978-0-08-051581-6.50065-9","DOI":"10.1016\/B978-0-08-051581-6.50065-9"},{"key":"14_CR12","doi-asserted-by":"publisher","unstructured":"Cherian, A., Sullivan, A.: Sem-GAN: semantically-consistent image-to-image translation. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1797\u20131806, January 2019. https:\/\/doi.org\/10.1109\/WACV.2019.00196","DOI":"10.1109\/WACV.2019.00196"},{"key":"14_CR13","unstructured":"Dinh, L., Krueger, D., Bengio, Y.: Nice: Non-linear independent components estimation (2014)"},{"key":"14_CR14","unstructured":"Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using real NVP (2016)"},{"key":"14_CR15","unstructured":"Fu, H., Gong, M., Wang, C., Batmanghelich, K., Zhang, K., Tao, D.: Geometry-consistent adversarial networks for one-sided unsupervised domain mapping. CoRR abs\/1809.05852 (2018). http:\/\/arxiv.org\/abs\/1809.05852"},{"key":"14_CR16","unstructured":"Gajane, P.: On formalizing fairness in prediction with machine learning. CoRR abs\/1710.03184 (2017). http:\/\/arxiv.org\/abs\/1710.03184"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Gecer, B., Bhattarai, B., Kittler, J., Kim, T.: Semi-supervised adversarial learning to generate photorealistic face images of new identities from 3D morphable model. CoRR abs\/1804.03675 (2018). http:\/\/arxiv.org\/abs\/1804.03675","DOI":"10.1007\/978-3-030-01252-6_14"},{"key":"14_CR18","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. CoRR abs\/1811.12231 (2018). http:\/\/arxiv.org\/abs\/1811.12231"},{"key":"14_CR19","unstructured":"Goodfellow, I.J., et al.: Generative Adversarial Networks. ArXiv e-prints (June 2014)"},{"key":"14_CR20","unstructured":"Gutmann, M., Hyv\u00e4rinen, A.: Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 9, pp. 297\u2013304. PMLR, Chia Laguna Resort, Sardinia, Italy, 13\u201315 May 2010. http:\/\/proceedings.mlr.press\/v9\/gutmann10a.html"},{"key":"14_CR21","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a nash equilibrium. CoRR abs\/1706.08500 (2017). http:\/\/arxiv.org\/abs\/1706.08500"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: International Conference on Computer Vision (ICCV), Venice, Italy (2017). https:\/\/vision.cornell.edu\/se3\/wp-content\/uploads\/2017\/08\/adain.pdf. oral","DOI":"10.1109\/ICCV.2017.167"},{"key":"14_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-030-01219-9_11","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Huang","year":"2018","unstructured":"Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part III. LNCS, vol. 11207, pp. 179\u2013196. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_11"},{"key":"14_CR24","unstructured":"Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. CoRR abs\/1611.07004 (2016). http:\/\/arxiv.org\/abs\/1611.07004"},{"key":"14_CR25","unstructured":"Jahanian, A., Chai, L., Isola, P.: On the \u201csteerability\u201d of generative adversarial networks. CoRR abs\/1907.07171 (2019). http:\/\/arxiv.org\/abs\/1907.07171"},{"key":"14_CR26","unstructured":"Jimenez Rezende, D., Mohamed, S., Wierstra, D.: Stochastic Backpropagation and Approximate Inference in Deep Generative Models. ArXiv e-prints (January 2014)"},{"key":"14_CR27","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. CoRR abs\/1812.04948 (2018). http:\/\/arxiv.org\/abs\/1812.04948"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN (2019)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"14_CR29","doi-asserted-by":"publisher","unstructured":"Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867\u20131874, June 2014. https:\/\/doi.org\/10.1109\/CVPR.2014.241","DOI":"10.1109\/CVPR.2014.241"},{"key":"14_CR30","unstructured":"Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. ArXiv e-prints (December 2013)"},{"key":"14_CR31","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"14_CR32","unstructured":"Kingma, D.P., Dhariwal, P.: Glow: Generative flow with invertible 1x1 convolutions (2018)"},{"key":"14_CR33","unstructured":"Larochelle, H., Murray, I.: The neural autoregressive distribution estimator. In: The Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. JMLR: W and CP, vol. 15, pp. 29\u201337 (2011)"},{"key":"14_CR34","unstructured":"Lin, J., Xia, Y., Liu, S., Qin, T., Chen, Z.: Zstgan: An adversarial approach for unsupervised zero-shot image-to-image translation. CoRR abs\/1906.00184 (2019). http:\/\/arxiv.org\/abs\/1906.00184"},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Liu, M.Y., et al.: Few-shot unsupervised image-to-image translation. In: The IEEE International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.01065"},{"key":"14_CR36","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/MRA.2012.2192811","volume":"19","author":"M Mori","year":"2012","unstructured":"Mori, M., MacDorman, K., Kageki, N.: The uncanny valley. IEEE Robot. Autom. Mag. 19, 98\u2013100 (2012). https:\/\/doi.org\/10.1109\/MRA.2012.2192811","journal-title":"IEEE Robot. Autom. Mag."},{"key":"14_CR37","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR abs\/1511.06434 (2015). http:\/\/arxiv.org\/abs\/1511.06434"},{"key":"14_CR38","unstructured":"Salimans, T., Goodfellow, I.J., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. CoRR abs\/1606.03498 (2016). http:\/\/arxiv.org\/abs\/1606.03498"},{"key":"14_CR39","unstructured":"Sohl-Dickstein, J., Weiss, E.A., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. CoRR abs\/1503.03585 (2015). http:\/\/arxiv.org\/abs\/1503.03585"},{"key":"14_CR40","unstructured":"Sutherland, D.J., et al.: Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. ArXiv e-prints (November 2016)"},{"key":"14_CR41","first-page":"2175","volume":"26","author":"B Uria","year":"2013","unstructured":"Uria, B., Murray, I., Larochelle, H.: RNADE: the real-valued neural autoregressive density-estimator. Adv. Neural Inf. Proc. Syst. 26, 2175\u20132183 (2013)","journal-title":"Adv. Neural Inf. Proc. Syst."},{"key":"14_CR42","unstructured":"Wang, C., Zheng, H., Yu, Z., Zheng, Z., Gu, Z., Zheng, B.: Discriminative region proposal adversarial networks for high-quality image-to-image translation. CoRR abs\/1711.09554 (2017). http:\/\/arxiv.org\/abs\/1711.09554"},{"key":"14_CR43","doi-asserted-by":"crossref","unstructured":"Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs (2017)","DOI":"10.1109\/CVPR.2018.00917"},{"key":"14_CR44","doi-asserted-by":"publisher","unstructured":"Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003, vol. 2, pp. 1398\u20131402, November 2003. https:\/\/doi.org\/10.1109\/ACSSC.2003.1292216","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"14_CR45","unstructured":"Wrenninge, M., Villemin, R., Hery, C.: Path traced subsurface scattering using anisotropic phase functions and non-exponential free flights. Technical report"},{"key":"14_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. CoRR abs\/1801.03924 (2018). http:\/\/arxiv.org\/abs\/1801.03924","DOI":"10.1109\/CVPR.2018.00068"},{"key":"14_CR47","unstructured":"Zhang, R., Pfister, T., Li, J.: Harmonic unpaired image-to-image translation. CoRR abs\/1902.09727 (2019). http:\/\/arxiv.org\/abs\/1902.09727"},{"key":"14_CR48","unstructured":"Zhao, S., Ren, H., Yuan, A., Song, J., Goodman, N.D., Ermon, S.: Bias and generalization in deep generative models: An empirical study. CoRR abs\/1811.03259 (2018). http:\/\/arxiv.org\/abs\/1811.03259"},{"key":"14_CR49","unstructured":"Zheng, Z., Yu, Z., Zheng, H., Yang, Y., Shen, H.T.: One-shot image-to-image translation via part-global learning with a multi-adversarial framework. CoRR abs\/1905.04729 (2019). http:\/\/arxiv.org\/abs\/1905.04729"},{"key":"14_CR50","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Computer Vision (ICCV), 2017 IEEE International Conference on (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58604-1_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:11:15Z","timestamp":1730506275000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58604-1_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586034","9783030586041"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58604-1_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}