{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:55:59Z","timestamp":1773510959863,"version":"3.50.1"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250620","type":"print"},{"value":"9783031250637","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25063-7_13","type":"book-chapter","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T20:10:15Z","timestamp":1676491815000},"page":"204-220","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Third Time\u2019s the\u00a0Charm? Image and\u00a0Video Editing with\u00a0StyleGAN3"],"prefix":"10.1007","author":[{"given":"Yuval","family":"Alaluf","sequence":"first","affiliation":[]},{"given":"Or","family":"Patashnik","sequence":"additional","affiliation":[]},{"given":"Zongze","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Asif","family":"Zamir","sequence":"additional","affiliation":[]},{"given":"Eli","family":"Shechtman","sequence":"additional","affiliation":[]},{"given":"Dani","family":"Lischinski","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,16]]},"reference":[{"key":"13_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 the IEEE International Conference on Computer Vision, pp. 4432\u20134441 (2019)","DOI":"10.1109\/ICCV.2019.00453"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2stylegan++: how to edit the embedded images? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 8296\u20138305 (2020)","DOI":"10.1109\/CVPR42600.2020.00832"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Abdal, R., Zhu, P., Mitra, N., Wonka, P.: Styleflow: attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows (2020)","DOI":"10.1145\/3447648"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"Alaluf, Y., Patashnik, O., Cohen-Or, D.: Only a matter of style: age transformation using a style-based regression model. ACM Trans. Graph. 40(4) (2021). https:\/\/doi.org\/10.1145\/3450626.3459805","DOI":"10.1145\/3450626.3459805"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Alaluf, Y., Patashnik, O., Cohen-Or, D.: Restyle: a residual-based stylegan encoder via iterative refinement. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2021","DOI":"10.1109\/ICCV48922.2021.00664"},{"key":"13_CR6","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 (2021)","DOI":"10.1109\/CVPR52688.2022.01796"},{"key":"13_CR7","unstructured":"Bau, D., et al.: Paint by word (2021)"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Bau, D., et al.: Semantic photo manipulation with a generative image prior 38(4) (2019). https:\/\/doi.org\/10.1145\/3306346.3323023, https:\/\/doi.org\/10.1145\/3306346.3323023","DOI":"10.1145\/3306346.3323023"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Choi, Y., Uh, Y., Yoo, J., Ha, J.W.: Stargan v2: diverse image synthesis for multiple domains (2020)","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Collins, E., Bala, R., Price, B., S\u00fcsstrunk, S.: Editing in style: uncovering the local semantics of GANs. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5770\u20135779 (2020)","DOI":"10.1109\/CVPR42600.2020.00581"},{"key":"13_CR11","unstructured":"Eastwood, C., Williams, C.K.: A framework for the quantitative evaluation of disentangled representations. In: International Conference on Learning Representations (2018)"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Gal, R., Patashnik, O., Maron, H., Chechik, G., Cohen-Or, D.: Stylegan-nada: clip-guided domain adaptation of image generators (2021)","DOI":"10.1145\/3528223.3530164"},{"key":"13_CR13","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2. NIPS 2014, Cambridge, MA, USA, pp. 2672\u20132680. MIT Press (2014)"},{"key":"13_CR14","unstructured":"Guan, S., Tai, Y., Ni, B., Zhu, F., Huang, F., Yang, X.: Collaborative learning for faster stylegan embedding. arXiv preprint arXiv:2007.01758 (2020)"},{"key":"13_CR15","unstructured":"H\u00e4rk\u00f6nen, E., Hertzmann, A., Lehtinen, J., Paris, S.: Ganspace: discovering interpretable GAN controls. arXiv preprint arXiv:2004.02546 (2020)"},{"key":"13_CR16","unstructured":"Hou, X., Zhang, X., Shen, L., Lai, Z., Wan, J.: Guidedstyle: Attribute knowledge guided style manipulation for semantic face editing (2020)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: Curricularface: adaptive curriculum learning loss for deep face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5901\u20135910 (2020)","DOI":"10.1109\/CVPR42600.2020.00594"},{"key":"13_CR18","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)"},{"key":"13_CR19","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data (2020)"},{"key":"13_CR20","unstructured":"Karras, T., et al.: Alias-free generative adversarial networks. CoRR abs\/2106.12423 (2021)"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"13_CR22","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 the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110\u20138119 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"13_CR23","first-page":"1755","volume":"10","author":"DE King","year":"2009","unstructured":"King, D.E.: DLIB-ML: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755\u20131758 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"13_CR24","unstructured":"Ling, H., Kreis, K., Li, D., Kim, S.W., Torralba, A., Fidler, S.: Editgan: high-precision semantic image editing. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Menon, S., Damian, A., Hu, S., Ravi, N., Rudin, C.: Pulse: self-supervised photo upsampling via latent space exploration of generative models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2437\u20132445 (2020)","DOI":"10.1109\/CVPR42600.2020.00251"},{"key":"13_CR27","unstructured":"Park, T., et al.: Swapping autoencoder for deep image manipulation. arXiv preprint arXiv:2007.00653 (2020)"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: Styleclip: text-driven manipulation of stylegan imagery (2021)","DOI":"10.1109\/ICCV48922.2021.00209"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Pidhorskyi, S., Adjeroh, D.A., Doretto, G.: Adversarial latent autoencoders. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14104\u201314113 (2020)","DOI":"10.1109\/CVPR42600.2020.01411"},{"key":"13_CR30","unstructured":"Rahaman, N., et al: On the spectral bias of neural networks. In: International Conference on Machine Learning, pp. 5301\u20135310. PMLR (2019)"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Richardson, E., et al.: Encoding in style: a stylegan encoder for image-to-image translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00232"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Roich, D., Mokady, R., Bermano, A.H., Cohen-Or, D.: Pivotal tuning for latent-based editing of real images. arXiv preprint arXiv:2106.05744 (2021)","DOI":"10.1145\/3544777"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of GANs for semantic face editing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9243\u20139252 (2020)","DOI":"10.1109\/CVPR42600.2020.00926"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Shen, Y., Zhou, B.: Closed-form factorization of latent semantics in GANs. arXiv preprint arXiv:2007.06600 (2020)","DOI":"10.1109\/CVPR46437.2021.00158"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Skorokhodov, I., Sotnikov, G., Elhoseiny, M.: Aligning latent and image spaces to connect the unconnectable. arXiv preprint arXiv:2104.06954 (2021)","DOI":"10.1109\/ICCV48922.2021.01388"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"Tewari, A., et al.: Stylerig: rigging stylegan for 3D control over portrait images. arXiv preprint arXiv:2004.00121 (2020)","DOI":"10.1109\/CVPR42600.2020.00618"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., Cohen-Or, D.: Designing an encoder for stylegan image manipulation (2021)","DOI":"10.1145\/3476576.3476706"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Tzaban, R., Mokady, R., Gal, R., Bermano, A.H., Cohen-Or, D.: Stitch it in time: gan-based facial editing of real videos (2022)","DOI":"10.1145\/3550469.3555382"},{"key":"13_CR39","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, vol. 2, pp. 1398\u20131402. IEEE (2003)"},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"Wu, Z., Lischinski, D., Shechtman, E.: Stylespace analysis: disentangled controls for stylegan image generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12863\u201312872 (2021)","DOI":"10.1109\/CVPR46437.2021.01267"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Xia, W., Zhang, Y., Yang, Y., Xue, J.H., Zhou, B., Yang, M.H.: Gan inversion: a survey (2021)","DOI":"10.1109\/TPAMI.2022.3181070"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Xu, Y., AlBahar, B., Huang, J.B.: Temporally consistent semantic video editing. arXiv e-prints pp. arXiv-2206 (2022)","DOI":"10.1007\/978-3-031-19784-0_21"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Yao, X., Newson, A., Gousseau, Y., Hellier, P.: A latent transformer for disentangled face editing in images and videos. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13789\u201313798 (2021)","DOI":"10.1109\/ICCV48922.2021.01353"},{"key":"13_CR44","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: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"13_CR45","doi-asserted-by":"crossref","unstructured":"Zhu, J., Shen, Y., Zhao, D., Zhou, B.: In-domain GAN inversion for real image editing. arXiv preprint arXiv:2004.00049 (2020)","DOI":"10.1007\/978-3-030-58520-4_35"},{"key":"13_CR46","doi-asserted-by":"publisher","unstructured":"Zhu, P., Abdal, R., Femiani, J., Wonka, P.: Barbershop: GAN-based image compositing using segmentation masks. ACM Trans. Graph. 40(6) (2021). https:\/\/doi.org\/10.1145\/3478513.3480537","DOI":"10.1145\/3478513.3480537"},{"key":"13_CR47","unstructured":"Zhu, P., Abdal, R., Qin, Y., Wonka, P.: Improved stylegan embedding: where are the good latents? ArXiv abs\/2012.09036 (2020)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25063-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:03:34Z","timestamp":1710270214000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25063-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250620","9783031250637"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25063-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 February 2023","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)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}