{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:15:13Z","timestamp":1775582113087,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.U19B2044"],"award-info":[{"award-number":["No.U19B2044"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61836011"],"award-info":[{"award-number":["No.61836011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s11263-024-02268-4","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T06:49:41Z","timestamp":1732258181000},"page":"2371-2391","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["One-Shot Generative Domain Adaptation in 3D GANs"],"prefix":"10.1007","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9484-2310","authenticated-orcid":false,"given":"Ziqiang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Rui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"2268_CR1","doi-asserted-by":"crossref","unstructured":"Abdal, R., Lee, HY., Zhu, P., Chai, M., Siarohin, A., Wonka, P., & Tulyakov, S. (2023). 3DAVATARGAN: Bridging domains for personalized editable avatars. arXiv preprint[SPACE]arXiv:2301.02700","DOI":"10.1109\/CVPR52729.2023.00442"},{"key":"2268_CR2","unstructured":"Alanov, A., Titov, V., & Vetrov, D. (2022). Hyperdomainnet: Universal domain adaptation for generative adversarial networks. arXiv preprint[SPACE]arXiv:2210.08884"},{"key":"2268_CR3","unstructured":"Bi\u0144kowski, M., Sutherland, D. J., Arbel, M., & Gretton, A. (2018). Demystifying mmd gans. arXiv preprint[SPACE]arXiv:1801.01401"},{"key":"2268_CR4","doi-asserted-by":"crossref","unstructured":"Chan, E. R., Lin, C. Z., Chan, M. A., Nagano, K., Pan, B., De\u00a0Mello, S., Gallo, O., Guibas, L. J., Tremblay, J., & Khamis, S., et\u00a0al. (2022). Efficient geometry-aware 3D generative adversarial networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 16123\u201316133).","DOI":"10.1109\/CVPR52688.2022.01565"},{"key":"2268_CR5","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). ARCFACE: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 4690\u20134699).","DOI":"10.1109\/CVPR.2019.00482"},{"key":"2268_CR6","doi-asserted-by":"crossref","unstructured":"DeVries, T., Bautista, M. A., Srivastava, N., Taylor, G. W., & Susskind, J. M. (2021). Unconstrained scene generation with locally conditioned radiance fields. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, (pp 14304\u201314313).","DOI":"10.1109\/ICCV48922.2021.01404"},{"key":"2268_CR7","doi-asserted-by":"crossref","unstructured":"Gal, R., Patashnik, O., Maron, H., Chechik, G., & Cohen-Or, D. (2021). Stylegan-nada: Clip-guided domain adaptation of image generators. arXiv preprint[SPACE]arXiv:2108.00946","DOI":"10.1145\/3528223.3530164"},{"key":"2268_CR8","doi-asserted-by":"crossref","unstructured":"Girin, L., Leglaive, S., Bie, X., Diard, J., Hueber, T., & Alameda-Pineda, X. (2020). Dynamical variational autoencoders: A comprehensive review. arXiv preprint[SPACE]arXiv:2008.12595","DOI":"10.1561\/9781680839135"},{"key":"2268_CR9","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems 27"},{"issue":"11","key":"2268_CR10","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139\u2013144.","journal-title":"Communications of the ACM"},{"key":"2268_CR11","unstructured":"Gu, J., Liu, L., Wang, P., & Theobalt, C. (2021). Stylenerf: A style-based 3D-aware generator for high-resolution image synthesis. arXiv preprint[SPACE]arXiv:2110.08985"},{"key":"2268_CR12","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems 30"},{"key":"2268_CR13","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840\u20136851.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"3","key":"2268_CR14","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1145\/964965.808594","volume":"18","author":"JT Kajiya","year":"1984","unstructured":"Kajiya, J. T., & Von Herzen, B. P. (1984). Ray tracing volume densities. ACM SIGGRAPH Computer Graphics, 18(3), 165\u2013174.","journal-title":"ACM SIGGRAPH Computer Graphics"},{"key":"2268_CR15","unstructured":"Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv preprint[SPACE]arXiv:1710.10196"},{"key":"2268_CR16","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 4401\u20134410).","DOI":"10.1109\/CVPR.2019.00453"},{"key":"2268_CR17","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T. (2020). Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 8110\u20138119).","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"2268_CR18","doi-asserted-by":"crossref","unstructured":"Kim, G., & Chun, S. Y. (2023). DATID-3D: Diversity-preserved domain adaptation using text-to-image diffusion for 3D generative model. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 14203\u201314213).","DOI":"10.1109\/CVPR52729.2023.01365"},{"key":"2268_CR19","doi-asserted-by":"crossref","unstructured":"Kim, G., Jang, J. H., & Chun, S. Y. (2023). Podia-3D: Domain adaptation of 3d generative model across large domain gap using pose-preserved text-to-image diffusion. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (pp. 22603\u201322612).","DOI":"10.1109\/ICCV51070.2023.02066"},{"key":"2268_CR20","doi-asserted-by":"crossref","unstructured":"Kim, S., Kang, K., Kim, G., Baek, S. H., & Cho, S. (2022). Dynagan: Dynamic few-shot adaptation of gans to multiple domains. In SIGGRAPH ASIA 2022 Conference Papers (pp. 1\u20138).","DOI":"10.1145\/3550469.3555416"},{"key":"2268_CR21","doi-asserted-by":"crossref","unstructured":"Ko, J., Cho, K., Choi, D., Ryoo, K., Kim, S. (2023). 3D GAN inversion with pose optimization. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (pp. 2967\u20132976).","DOI":"10.1109\/WACV56688.2023.00298"},{"key":"2268_CR22","doi-asserted-by":"crossref","unstructured":"Kolkin, N., Salavon, J., Shakhnarovich, G. (2019). Style transfer by relaxed optimal transport and self-similarity. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 10051\u201310060).","DOI":"10.1109\/CVPR.2019.01029"},{"key":"2268_CR23","doi-asserted-by":"crossref","unstructured":"Kwon, G., & Ye, J. C. (2023). One-shot adaptation of GAN in just one clip. In IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2023.3283551"},{"key":"2268_CR24","doi-asserted-by":"crossref","unstructured":"Li, J., Li, J., Zhang, H., Liu, S., Wang, Z., Xiao, Z., Zheng, K., & Zhu, J. (2023a). PREIM3D: 3D consistent precise image attribute editing from a single image. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 8549\u20138558).","DOI":"10.1109\/CVPR52729.2023.00826"},{"key":"2268_CR25","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, C., Zheng, H., Zhang, J., & Li, B. (2022a). FAKECLR: Exploring contrastive learning for solving latent discontinuity in data-efficient GANS. In European Conference on Computer Vision. Springer (pp. 598\u2013615).","DOI":"10.1007\/978-3-031-19784-0_35"},{"issue":"1","key":"2268_CR26","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1109\/TETCI.2022.3193373","volume":"7","author":"Z Li","year":"2022","unstructured":"Li, Z., Xia, P., Tao, R., Niu, H., & Li, B. (2022). A new perspective on stabilizing GANS training: Direct adversarial training. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(1), 178\u2013189.","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"issue":"11","key":"2268_CR27","first-page":"1","volume":"55","author":"Z Li","year":"2023","unstructured":"Li, Z., Usman, M., Tao, R., Xia, P., Wang, C., Chen, H., & Li, B. (2023). A systematic survey of regularization and normalization in GANS. ACM Computing Surveys, 55(11), 1\u201337.","journal-title":"ACM Computing Surveys"},{"key":"2268_CR28","unstructured":"Li, Z., Wang, C., Rui, X., Xue, C., Leng, J., & Li, B. (2023c). Peer is your pillar: A data-unbalanced conditional GANS for few-shot image generation. arXiv preprint[SPACE]arXiv:2311.08217"},{"issue":"5","key":"2268_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3579998","volume":"19","author":"Z Li","year":"2023","unstructured":"Li, Z., Xia, P., Rui, X., & Li, B. (2023). Exploring the effect of high-frequency components in GANS training. ACM Transactions on Multimedia Computing, Communications, and Applications, 19(5), 1\u201322.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications"},{"key":"2268_CR30","doi-asserted-by":"crossref","unstructured":"Lin, C. H., Gao, J., Tang, L., Takikawa, T., Zeng, X., Huang, X., Kreis, K., Fidler, S., Liu, M. Y., & Lin, T. Y. (2023). MAGIC3D: High-resolution text-to-3D content creation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 300\u2013309).","DOI":"10.1109\/CVPR52729.2023.00037"},{"issue":"2","key":"2268_CR31","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/2945.468400","volume":"1","author":"N Max","year":"1995","unstructured":"Max, N. (1995). Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 1(2), 99\u2013108.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"issue":"1","key":"2268_CR32","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2021). Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1), 99\u2013106.","journal-title":"Communications of the ACM"},{"key":"2268_CR33","doi-asserted-by":"crossref","unstructured":"Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. L. (2019). HOLOGAN: Unsupervised learning of 3D representations from natural images. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (pp. 7588\u20137597).","DOI":"10.1109\/ICCV.2019.00768"},{"key":"2268_CR34","first-page":"6767","volume":"33","author":"TH Nguyen-Phuoc","year":"2020","unstructured":"Nguyen-Phuoc, T. H., Richardt, C., Mai, L., Yang, Y., & Mitra, N. (2020). BLOCKGAN: Learning 3D object-aware scene representations from unlabelled images. Advances in Neural Information Processing Systems, 33, 6767\u20136778.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2268_CR35","unstructured":"Nichol, A. Q., & Dhariwal, P. (2021). Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, PMLR. (pp. 8162\u20138171)."},{"key":"2268_CR36","doi-asserted-by":"crossref","unstructured":"Ojha, U., Li, Y., Lu, J., Efros, A. A., Lee, Y. J., Shechtman, E., & Zhang, R. (2021). Few-shot image generation via cross-domain correspondence. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 10743\u201310752).","DOI":"10.1109\/CVPR46437.2021.01060"},{"key":"2268_CR37","doi-asserted-by":"crossref","unstructured":"Or-El, R., Luo, X., Shan, M., Shechtman, E., Park, J. J., & Kemelmacher-Shlizerman, I. (2022). STYLESDF: High-resolution 3d-consistent image and geometry generation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (pp. 13503\u201313513).","DOI":"10.1109\/CVPR52688.2022.01314"},{"key":"2268_CR38","unstructured":"Pinkney, J. N., & Adler, D. (2020). Resolution dependent gan interpolation for controllable image synthesis between domains. arXiv preprint [SPACE]arXiv:2010.05334"},{"key":"2268_CR39","unstructured":"Poole, B., Jain, A., Barron, J. T., & Mildenhall, B. (2022). Dreamfusion: Text-to-3D using 2D diffusion. arXiv preprint arXiv:2209.14988"},{"key":"2268_CR40","unstructured":"Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., & Clark, J., et\u00a0al. (2021). Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, PMLR (pp. 8748\u20138763)."},{"key":"2268_CR41","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684\u201310695).","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"2268_CR42","doi-asserted-by":"crossref","unstructured":"Ruiz, N., Li, Y., Jampani. V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023). Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 22500\u201322510).","DOI":"10.1109\/CVPR52729.2023.02155"},{"key":"2268_CR43","doi-asserted-by":"crossref","unstructured":"Shechtman, E., & Irani, M. (2007). Matching local self-similarities across images and videos. In 2007 IEEE Conference on Computer Vision and Pattern Recognition, IEEE (pp. 1\u20138).","DOI":"10.1109\/CVPR.2007.383198"},{"key":"2268_CR44","unstructured":"Skorokhodov, I., Tulyakov, S., Wang, Y., & Wonka, P. (2022). EPIGRAF: Rethinking training of 3D gans. arXiv preprint[SPACE]arXiv:2206.10535"},{"key":"2268_CR45","unstructured":"Song, K., Han, L., Liu, B., Metaxas, D., & Elgammal, A. (2022). Diffusion guided domain adaptation of image generators. arXiv preprint[SPACE]arXiv:2212.04473"},{"key":"2268_CR46","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, B., Zhang, T., Gu, S., Bao, J., Baltrusaitis, T., Shen, J., Chen, D., Wen, F., & Chen, Q., et\u00a0al. (2023). Rodin: A generative model for sculpting 3D digital avatars using diffusion. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 4563\u20134573).","DOI":"10.1109\/CVPR52729.2023.00443"},{"issue":"11","key":"2268_CR47","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1109\/TPAMI.2008.222","volume":"31","author":"X Wang","year":"2008","unstructured":"Wang, X., & Tang, X. (2008). Face photo-sketch synthesis and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 1955\u20131967.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2268_CR48","unstructured":"Wu, H., Zhang, Z., Zhang, W., Chen, C., Liao, L., Li, C., Gao, Y., Wang, A., Zhang, E., & Sun, W. et\u00a0al. (2023a). Q-align: Teaching LMMS for visual scoring via discrete text-defined levels. arXiv preprint[SPACE]arXiv:2312.17090"},{"key":"2268_CR49","first-page":"57099","volume":"36","author":"Y Wu","year":"2023","unstructured":"Wu, Y., Li, Z., Wang, C., Zheng, H., Zhao, S., Li, B., & Tao, D. (2023). Domain re-modulation for few-shot generative domain adaptation. Advances in Neural Information Processing Systems, 36, 57099\u201357124.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2268_CR50","doi-asserted-by":"crossref","unstructured":"Wu, Y., Li, Z., Zheng, H., Wang, C., & Li, B. (2024). Infinite-id: Identity-preserved personalization via id-semantics decoupling paradigm. arXiv preprint[SPACE]arXiv:2403.11781","DOI":"10.1007\/978-3-031-73242-3_16"},{"key":"2268_CR51","doi-asserted-by":"crossref","unstructured":"Xiao, J., Li, L., Wang, C., Zha, Z. J., & Huang, Q. (2022). Few shot generative model adaption via relaxed spatial structural alignment. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 11204\u201311213).","DOI":"10.1109\/CVPR52688.2022.01092"},{"key":"2268_CR52","unstructured":"Yang, C., Shen, Y., Xu, Y., Zhao, D., Dai, B., & Zhou, B. (2022a). Improving gans with a dynamic discriminator. arXiv preprint[SPACE]arXiv:2209.09897"},{"key":"2268_CR53","doi-asserted-by":"crossref","unstructured":"Yang, C., Shen, Y., Zhang, Z., Xu, Y., Zhu, J., Wu, Z., & Zhou, B. (2023). One-shot generative domain adaptation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (pp. 7733\u20137742).","DOI":"10.1109\/ICCV51070.2023.00711"},{"key":"2268_CR54","doi-asserted-by":"crossref","unstructured":"Yang, S., Jiang, L., Liu, Z., & Loy, C. C. (2022b). Pastiche master: Exemplar-based high-resolution portrait style transfer. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 7693\u20137702).","DOI":"10.1109\/CVPR52688.2022.00754"},{"key":"2268_CR55","unstructured":"Zhang, C., Chen, Y., Fu, Y., Zhou, Z., Yu, G., Wang, B., Fu, B., Chen, T., & Lin, G., Shen, C. (2023a). STYLEAVATAR3D: Leveraging image-text diffusion models for high-fidelity 3D avatar generation. arXiv preprint[SPACE]arXiv:2305.19012"},{"key":"2268_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhai, G., Wei, Y., Yang, X., & Ma, K. (2023b). Blind image quality assessment via vision-language correspondence: A multitask learning perspective. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 14071\u201314081).","DOI":"10.1109\/CVPR52729.2023.01352"},{"key":"2268_CR57","unstructured":"Zhang, Y., Wei, Y., Ji, Z., Bai, J., & Zuo, W., et\u00a0al. (2022a). Towards diverse and faithful one-shot adaption of generative adversarial networks. In Advances in Neural Information Processing Systems."},{"key":"2268_CR58","unstructured":"Zhang, Z., Liu, Y., Han, C., Guo, T., Yao, T., & Mei, T. (2022b). Generalized one-shot domain adaptation of generative adversarial networks. In Advances in Neural Information Processing Systems."},{"key":"2268_CR59","unstructured":"Zhao, S., Song, J., & Ermon, S. (2017). Infovae: Information maximizing variational autoencoders. arXiv preprint[SPACE]arXiv:1706.02262"},{"key":"2268_CR60","doi-asserted-by":"crossref","unstructured":"Zhao, X., Ma, F., G\u00fcera, D., Ren, Z., Schwing, A. G., & Colburn, A. (2022). Generative multiplane images: Making a 2D GAN 3D-aware. In Computer Vision\u2013ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part V, Springer (pp. 18\u201335).","DOI":"10.1007\/978-3-031-20065-6_2"},{"key":"2268_CR61","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ding, H., Huang, H., & Cheung, N. M. (2022). A closer look at few-shot image generation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 9140\u20139150).","DOI":"10.1109\/CVPR52688.2022.00893"},{"key":"2268_CR62","unstructured":"Zhu, P., Abdal, R., Femiani, J., & Wonka, P. (2021). Mind the gap: Domain gap control for single shot domain adaptation for generative adversarial networks. arXiv preprint[SPACE]arXiv:2110.08398"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02268-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02268-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02268-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T06:00:26Z","timestamp":1744869626000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02268-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":62,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["2268"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02268-4","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,22]]},"assertion":[{"value":"3 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}