{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:24:12Z","timestamp":1743017052534,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200496"},{"type":"electronic","value":"9783031200502"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20050-2_12","type":"book-chapter","created":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T22:09:58Z","timestamp":1666908598000},"page":"189-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ChunkyGAN: Real Image Inversion via\u00a0Segments"],"prefix":"10.1007","author":[{"given":"Ad\u00e9la","family":"\u0160ubrtov\u00e1","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Futschik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"\u010cech","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michal","family":"Luk\u00e1\u010d","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eli","family":"Shechtman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"S\u00fdkora","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN: how to embed images into the StyleGAN latent space? In: Proceedings of IEEE International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00453"},{"issue":"3","key":"12_CR2","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1145\/3447648","volume":"40","author":"R Abdal","year":"2021","unstructured":"Abdal, R., Zhu, P., Mitra, N.J., Wonka, P.: StyleFlow: attribute-conditioned exploration of styleGAN-generated images using conditional continuous normalizing flows. ACM Trans. Graph. 40(3), 21 (2021)","journal-title":"ACM Trans. Graph."},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Alaluf, Y., Patashnik, O., Cohen-Or, D.: ReStyle: a residual-based StyleGAN encoder via iterative refinement. In: Proceedings of IEEE International Conference on Computer Vision, pp. 6711\u20136720 (2021)","DOI":"10.1109\/ICCV48922.2021.00664"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Alaluf, Y., Tov, O., Mokady, R., Gal, R., Bermano, A.H.: HyperStyle: StyleGAN inversion with hypernetworks for real image editing. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 18511\u201318521 (2022)","DOI":"10.1109\/CVPR52688.2022.01796"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Bau, D., et al.: Seeing what a GAN cannot generate. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4501\u20134510 (2019)","DOI":"10.1109\/ICCV.2019.00460"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4685\u20134694 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Dinh, T.M., Tran, A.T., Nguyen, R., Hua, B.S.: HyperInverter: improving styleGAN inversion via hypernetwork. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 11389\u201311398 (2022)","DOI":"10.1109\/CVPR52688.2022.01110"},{"issue":"4","key":"12_CR8","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1145\/3072959.3073660","volume":"36","author":"J Fi\u0161er","year":"2017","unstructured":"Fi\u0161er, J., et al.: Example-based synthesis of stylized facial animations. ACM Trans. Graph. 36(4), 155 (2017)","journal-title":"ACM Trans. Graph."},{"key":"12_CR9","unstructured":"Futschik, D., Luk\u00e1\u010d, M., Shechtman, E., S\u00fdkora, D.: Real image inversion via segments. In: arXiv. No. 2110.06269 (2021)"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Huh, M., Zhang, R., Zhu, J.Y., Paris, S., Hertzmann, A.: Transforming and projecting images into class-conditional generative networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 17\u201334 (2020)","DOI":"10.1007\/978-3-030-58536-5_2"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Kang, K., Kim, S., Cho, S.: GAN inversion for out-of-range images with geometric transformations. In: Proceedings of IEEE International Conference on Computer Vision, pp. 13941\u201313949 (2021)","DOI":"10.1109\/ICCV48922.2021.01368"},{"key":"12_CR12","unstructured":"Karras, T., et al.: Alias-free generative adversarial networks. In: Proceedings of Conference on Neural Information Processing Systems (2021)"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"12_CR14","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. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 8107\u20138116 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Lee, C.H., Liu, Z., Wu, L., Luo, P.: MaskGAN: towards diverse and interactive facial image manipulation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5549\u20135558 (2020)","DOI":"10.1109\/CVPR42600.2020.00559"},{"key":"12_CR16","unstructured":"Ling, H., Kreis, K., Li, D., Kim, S.W., Torralba, A., Fidler, S.: EditGAN: high-precision semantic image editing. In: Proceedings of Conference on Neural Information Processing Systems (2021)"},{"key":"12_CR17","unstructured":"Lipton, Z.C., Tripathi, S.: Precise recovery of latent vectors from generative adversarial networks. In: Proceedings of International Conference on Learning Representations (2017)"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: StyleCLIP: text-driven manipulation of StyleGAN imagery. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2085\u20132094 (2021)","DOI":"10.1109\/ICCV48922.2021.00209"},{"issue":"3","key":"12_CR19","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1145\/882262.882269","volume":"22","author":"P P\u00e9rez","year":"2003","unstructured":"P\u00e9rez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Trans. Graph. 22(3), 313\u2013318 (2003)","journal-title":"ACM Trans. Graph."},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Richardson, E., et al.: Encoding in style: a styleGAN encoder for image-to-image translation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2288\u20132296 (2021)","DOI":"10.1109\/CVPR46437.2021.00232"},{"key":"12_CR21","unstructured":"Roich, D., Mokady, R., Bermano, A.H., Cohen-Or, D.: Pivotal tuning for latent-based editing of real images. In: arXiv. No. 2106.05744 (2021)"},{"issue":"4","key":"12_CR22","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1145\/3450626.3459838","volume":"40","author":"O Tov","year":"2021","unstructured":"Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., Cohen-Or, D.: Designing an encoder for StyleGAN image manipulation. ACM Trans. Graph. 40(4), 133 (2021)","journal-title":"ACM Trans. Graph."},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Wu, Z., Lischinski, D., Shechtman, E.: StyleSpace analysis: disentangled controls for StyleGAN image generation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 12863\u201312872 (2021)","DOI":"10.1109\/CVPR46437.2021.01267"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Xu, Y., Du, Y., Xiao, W., Xu, X., He, S.: From continuity to editability: inverting GANs with consecutive images. In: Proceedings of IEEE International Conference on Computer Vision, pp. 13910\u201313918 (2021)","DOI":"10.1109\/ICCV48922.2021.01365"},{"key":"12_CR25","unstructured":"Yao, X., Newson, A., Gousseau, Y., Hellier, P.: Feature-style encoder for style-based GAN inversion. In: arXiv. No. 2202.02183 (2022)"},{"key":"12_CR26","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. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: DatasetGAN: efficient labeled data factory with minimal human effort. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 10145\u201310155 (2021)","DOI":"10.1109\/CVPR46437.2021.01001"},{"key":"12_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1007\/978-3-030-58520-4_35","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Shen, Y., Zhao, D., Zhou, B.: In-domain GAN inversion for real image editing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 592\u2013608. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_35"},{"key":"12_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-46454-1_36","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J-Y Zhu","year":"2016","unstructured":"Zhu, J.-Y., Kr\u00e4henb\u00fchl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 597\u2013613. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_36"},{"issue":"6","key":"12_CR30","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1145\/3478513.3480537","volume":"40","author":"P Zhu","year":"2021","unstructured":"Zhu, P., Abdal, R., Femiani, J., Wonka, P.: Barbershop: GAN-based image compositing using segmentation masks. ACM Trans. Graph. 40(6), 215 (2021)","journal-title":"ACM Trans. Graph."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20050-2_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T22:24:00Z","timestamp":1666909440000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20050-2_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200496","9783031200502"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20050-2_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}