{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T17:09:26Z","timestamp":1781197766944,"version":"3.54.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":75,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Natural Science Foundation of Chongqing Joint Fund for Innovation and Development","award":["CSTB2025NSCQ-LZX0134"],"award-info":[{"award-number":["CSTB2025NSCQ-LZX0134"]}]},{"name":"the Science and Research Development Center of the Ministry of Education","award":["2023ZY024"],"award-info":[{"award-number":["2023ZY024"]}]},{"name":"the Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJZD-K202400809"],"award-info":[{"award-number":["KJZD-K202400809"]}]},{"DOI":"10.13039\/501100005230","name":"the Natural Science Foundation of Chongqing","doi-asserted-by":"crossref","award":["CSTB2023NSCQ-LZX0029"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0029"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Graduate Innovation Project of Chongqing Technology and Business University","award":["yjscxx2025-269-181"],"award-info":[{"award-number":["yjscxx2025-269-181"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Latent space-based facial attribute editing methods have gained popularity in applications such as digital entertainment, virtual avatar creation, and human-computer interaction systems due to their potential for efficient and flexible attribute manipulation, particularly for continuous edits. Among these, unsupervised latent space-based methods, which discover effective semantic vectors without relying on labeled data, have attracted considerable attention in the research community. However, existing methods still encounter difficulties in disentanglement, as manipulating a specific facial attribute may unintentionally affect other attributes, complicating fine-grained controllability. To address these challenges, we propose a novel framework designed to offer an effective and adaptable solution for unsupervised facial attribute editing, called Unsupervised Facial Attribute Controllable Editing (U-Face). The proposed method frames semantic vector learning as a subspace learning problem, where latent vectors are approximated within a lower-dimensional semantic subspace spanned by a semantic vector matrix. This formulation can also be equivalently interpreted from a projection-reconstruction perspective and further generalized into an autoencoder framework, providing a foundation that can support disentangled representation learning in a flexible manner. To improve disentanglement and controllability, we impose orthogonal non-negative constraints on the semantic vectors and incorporate attribute boundary vectors to reduce entanglement in the learned directions. Although these constraints make the optimization problem challenging, we design an alternating iterative algorithm, called Alternating Iterative Disentanglement and Controllability (AIDC), with closed-form updates and provable convergence under specific conditions. Extensive experiments on multiple pre-trained GAN generators demonstrate that U-Face shows improved disentanglement, controllability, and visual fidelity compared to existing state-of-the-art supervised and unsupervised baselines. Specifically, U-Face reduces inter-attribute correlation by an average of 5%-15%, and improves FID by 10%-20% and LPIPS by 5%-10%. Compared to recent diffusion-based methods (DDS, CDS, FPE), U-Face achieves comparable editing quality and reduces inference times, making it suitable for real-time applications across different GAN backbones and enabling large-scale interactive facial editing.<\/jats:p>","DOI":"10.1007\/s44443-026-00575-z","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T08:06:50Z","timestamp":1772179610000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["U-Face: An efficient and generalizable framework for unsupervised facial attribute editing via subspace learning"],"prefix":"10.1007","volume":"38","author":[{"given":"Bo","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Run","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongwen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinrui","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianggui","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongping","family":"Gan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"575_CR1","unstructured":"Chai L, Wulff J, Isola P (2021) Using latent space regression to analyze and leverage compositionality in gan. In: Proceedings of international conference on learning representations"},{"key":"575_CR2","doi-asserted-by":"crossref","unstructured":"Chang J, Li X (2025) Sp-difffusion: Diffusion-based infrared-visible fusion via subspace learning. In: 2025 5th International symposium on computer technology and information science, pp 281\u2013286","DOI":"10.1109\/ISCTIS65944.2025.11065936"},{"key":"575_CR3","unstructured":"Chen X, Lathuiliere S (2023) Face aging via diffusion-based editing. ArXiv:2309.11321"},{"key":"575_CR4","doi-asserted-by":"publisher","first-page":"37175","DOI":"10.1109\/JIOT.2025.3582636","volume":"12","author":"Z Chen","year":"2025","unstructured":"Chen Z, Yang J, Chen L, Li F, Feng Z, Jia L, Li P (2025) Railvoxeldet: A lightweight 3-d object detection method for railway transportation driven by onboard lidar data. IEEE Internet Things J 12:37175\u201337189","journal-title":"IEEE Internet Things J"},{"key":"575_CR5","doi-asserted-by":"crossref","unstructured":"Cherepkov A, Voynov A, Babenko A (2021) Navigating the gan parameter space for semantic image editing. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 3670\u20133679","DOI":"10.1109\/CVPR46437.2021.00367"},{"key":"575_CR6","unstructured":"Choi J, Lee J, Yoon C, Park JH, Hwang G, Kang M (2022) Do not escape from the manifold: Discovering the local coordinates on the latent space of GANs. In: Proceedings of international conference on learning representations"},{"key":"575_CR7","doi-asserted-by":"crossref","unstructured":"Choi Y, Uh Y, Yoo J, Ha J-W (2019) Stargan v2: Diverse image synthesis for multiple domains. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8185\u20138194","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"575_CR8","doi-asserted-by":"publisher","first-page":"14777","DOI":"10.1109\/TPAMI.2023.3308102","volume":"45","author":"Y Dalva","year":"2023","unstructured":"Dalva Y, Pehlivan H, Hatipoglu OI, Moran C, Dundar A (2023) Image-to-image translation with disentangled latent vectors for face editing. IEEE Trans Pattern Anal Mach Intell 45:14777\u201314788","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"575_CR9","doi-asserted-by":"crossref","unstructured":"Davis KM, de La Torre-Ortiz C, Ruotsalo T (2022) Brain-supervised image editing. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 18459\u201318468","DOI":"10.1109\/CVPR52688.2022.01793"},{"key":"575_CR10","doi-asserted-by":"crossref","unstructured":"Ding G, Han X, Wang S, Wu S, Jin X, Tu D, Huang Q (2022) Attribute group editing for reliable few-shot image generation. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 11184\u201311193","DOI":"10.1109\/CVPR52688.2022.01091"},{"key":"575_CR11","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.neucom.2020.03.071","volume":"401","author":"Y Dogan","year":"2020","unstructured":"Dogan Y, Keles HY (2020) Semi-supervised image attribute editing using generative adversarial networks. Neurocomputing 401:338\u2013352","journal-title":"Neurocomputing"},{"key":"575_CR12","doi-asserted-by":"crossref","unstructured":"Feng K, Ma Y, Wang B, Qi C, Chen H, Chen Q, Wang Z (2025) Dit4edit: diffusion transformer for image editing. In: Proceedings of the thirty-ninth AAAI conference on artificial intelligence and thirty-seventh conference on innovative applications of artificial intelligence and fifteenth symposium on educational advances in artificial intelligence","DOI":"10.1609\/aaai.v39i3.32304"},{"key":"575_CR13","doi-asserted-by":"crossref","unstructured":"Garrido P, Valgaerts L, Rehmsen O, Thormaehlen T, Perez P, Theobalt C (2014) Automatic face reenactment. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 4217\u20134224","DOI":"10.1109\/CVPR.2014.537"},{"key":"575_CR14","doi-asserted-by":"crossref","unstructured":"Guo Q, Gu X (2024) Enhancing accuracy, diversity, and random input compatibility in face attribute manipulation. Eng Appl Artif Intell 108683","DOI":"10.1016\/j.engappai.2024.108683"},{"key":"575_CR15","doi-asserted-by":"crossref","unstructured":"Han D, Kim J (2015) Unsupervised simultaneous orthogonal basis clustering feature selection. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 5016\u20135023","DOI":"10.1109\/CVPR.2015.7299136"},{"key":"575_CR16","doi-asserted-by":"crossref","unstructured":"Han Y, Yang J, Fu Y (2021) Disentangled face attribute editing via instance-aware latent space search. In: Proceedings of the thirtieth international joint conference on artificial intelligence, pp 715\u2013721","DOI":"10.24963\/ijcai.2021\/99"},{"key":"575_CR17","first-page":"9841","volume":"33","author":"E H\u00e4rk\u00f6nen","year":"2020","unstructured":"H\u00e4rk\u00f6nen E, Hertzmann A, Lehtinen J, Paris S (2020) Ganspace: Discovering interpretable gan controls. Adv Neural Inf Process Syst 33:9841\u20139850","journal-title":"Adv Neural Inf Process Syst"},{"key":"575_CR18","doi-asserted-by":"crossref","unstructured":"Hertz A, Aberman K, Cohen-Or D (2023) Delta denoising score. in: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2328\u20132337","DOI":"10.1109\/ICCV51070.2023.00221"},{"key":"575_CR19","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. In: Proceedings of advances in neural information processing systems, vol 30, pp 6629\u20136640"},{"key":"575_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"575_CR21","doi-asserted-by":"publisher","first-page":"5464","DOI":"10.1109\/TIP.2019.2916751","volume":"28","author":"Z He","year":"2019","unstructured":"He Z, Zuo W, Kan M, Shan S, Chen X (2019) Attgan: Facial attribute editing by only changing what you want. IEEE Trans Image Process Publ IEEE Signal Process Soc 28:5464\u20135478","journal-title":"IEEE Trans Image Process Publ IEEE Signal Process Soc"},{"key":"575_CR22","doi-asserted-by":"crossref","unstructured":"Hou Y, Wang M, Zhao J (2025) Zero-shot face editing via id-attribute decoupled inversion In: 2025 IEEE international conference on multimedia and Expo (ICME), pp 1\u20136","DOI":"10.1109\/ICME59968.2025.11209805"},{"key":"575_CR23","doi-asserted-by":"crossref","unstructured":"Hu S-M, Zhang F-L, Wang M, Martin RR, Wang J (2013) Patchnet: a patch-based image representation for interactive library-driven image editing. ACM Trans Graph","DOI":"10.1145\/2508363.2508381"},{"key":"575_CR24","doi-asserted-by":"crossref","unstructured":"Huang W, Luo W, Huang J, Cao X (2024) Sdgan: Disentangling semantic manipulation for facial attribute editing. In: AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v38i3.28012"},{"key":"575_CR25","doi-asserted-by":"crossref","unstructured":"Huang Z, Ma S, Zhang J, Shan H (2023) Adaptive nonlinear latent transformation for conditional face editing. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 21022\u201321031","DOI":"10.1109\/ICCV51070.2023.01922"},{"key":"575_CR26","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"575_CR27","unstructured":"Jahanian A, Chai L, Isola P (2020) On the steerability of generative adversarial networks. In: Proceedings of international conference on learning representations"},{"key":"575_CR28","doi-asserted-by":"publisher","first-page":"4413","DOI":"10.1109\/TNNLS.2024.3377436","volume":"36","author":"H Jiang","year":"2025","unstructured":"Jiang H, Luo X, Yin J, Fu H, Wang F (2025) Orthogonal subspace representation for generative adversarial networks. IEEE Trans Neural Netw Learn Syst 36:4413\u20134427","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"575_CR29","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation. In: Proceedings of international conference on learning representations"},{"key":"575_CR30","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\/CV conference on computer vision and pattern recognition, pp 4401\u20134410","DOI":"10.1109\/CVPR.2019.00453"},{"key":"575_CR31","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":"575_CR32","doi-asserted-by":"crossref","unstructured":"Kemelmacher-Shlizerman I, Suwajanakorn S, Seitz SM (2014) Illumination-aware age progression. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 3334\u20133341","DOI":"10.1109\/CVPR.2014.426"},{"key":"575_CR33","doi-asserted-by":"crossref","unstructured":"Kim G, Kwon T, Ye JC (2022) Diffusionclip: Text-guided diffusion models for robust image manipulation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2416\u20132425","DOI":"10.1109\/CVPR52688.2022.00246"},{"key":"575_CR34","doi-asserted-by":"publisher","first-page":"103916","DOI":"10.1016\/j.dsp.2023.103916","volume":"134","author":"B Li","year":"2023","unstructured":"Li B, Deng S-H, Liu B, Li Y, He Z-F, Lai Y-K, Zhang C, Chen Z (2023) Controllable facial attribute editing via gaussian mixture model disentanglement. Digit Signal Process 134:103916","journal-title":"Digit Signal Process"},{"key":"575_CR35","doi-asserted-by":"crossref","unstructured":"Li W, Ding W, Sadasivam R, Cui X, Chen P (2019) His-gan: A histogram-based gan model to improve data generation quality. Neural Netw 31\u201345","DOI":"10.1016\/j.neunet.2019.07.001"},{"key":"575_CR36","doi-asserted-by":"crossref","unstructured":"Li B, Huang Z, Shan H, Zhang J (2024) Semantic latent decomposition with normalizing flows for face editing. In: ICASSP 2024\u20132024 IEEE international conference on acoustics, speech and signal processing, pp 4165\u20134169","DOI":"10.1109\/ICASSP48485.2024.10446836"},{"key":"575_CR37","doi-asserted-by":"crossref","unstructured":"Lin P, Liu B, Wang L, Lei Z, Cheng J (2021) Face translation based on semantic style transfer and rendering from one single image. In: Proceedings of the 2021 10th international conference on software and computer applications, pp 166\u2013172","DOI":"10.1145\/3457784.3457811"},{"key":"575_CR38","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1109\/TIP.2022.3142527","volume":"31","author":"K Liu","year":"2022","unstructured":"Liu K, Cao G, Zhou F, Liu B, Duan J, Qiu G (2022) Towards disentangling latent space for unsupervised semantic face editing. IEEE Trans Image Process 31:1475\u20131489","journal-title":"IEEE Trans Image Process"},{"key":"575_CR39","doi-asserted-by":"crossref","unstructured":"Liu M, Ding Y, Xia M, Liu X, Ding E, Zuo W, Wen S (2019) Stgan: A unified selective transfer network for arbitrary image attribute editing. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 3668\u20133677","DOI":"10.1109\/CVPR.2019.00379"},{"key":"575_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111695","volume":"294","author":"B Liu","year":"2024","unstructured":"Liu B, Li W, Li J, Cui X, Liu C, Gan H (2024) Attention non-negative spectral clustering. Knowl-Based Syst 294:111695","journal-title":"Knowl-Based Syst"},{"key":"575_CR41","doi-asserted-by":"crossref","unstructured":"Liu B, Wang C, Cao T, Jia K, Huang J (2024) Towards understanding cross and self-attention in stable diffusion for text-guided image editing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7817\u20137826","DOI":"10.1109\/CVPR52733.2024.00747"},{"key":"575_CR42","doi-asserted-by":"crossref","unstructured":"Li K, Xu F, Wang J, Dai Q, Liu Y (2012) A data-driven approach for facial expression synthesis in video. In: Proceedings of the IEEE\/CV conference on computer vision and pattern recognition, pp 57\u201364","DOI":"10.1109\/CVPR.2012.6247658"},{"key":"575_CR43","doi-asserted-by":"publisher","first-page":"3557","DOI":"10.1109\/TPAMI.2024.3350004","volume":"46","author":"A Melnik","year":"2024","unstructured":"Melnik A, Miasayedzenkau M, Makarovets D, Pirshtuk D, Akbulut E, Holzmann D, Renusch T, Reichert G, Ritter HJ (2024) Face generation and editing with stylegan: A survey. IEEE Trans Pattern Anal Mach Intell 46:3557\u20133576","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"575_CR44","doi-asserted-by":"crossref","unstructured":"Nam H, Kwon G, Park GY, Ye JC (2024) Contrastive denoising score for text-guided latent diffusion image editing, in: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9192\u20139201","DOI":"10.1109\/CVPR52733.2024.00878"},{"key":"575_CR45","doi-asserted-by":"crossref","unstructured":"Naveh C (2023) Multi-directional subspace editing in style-space. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7104\u20137114","DOI":"10.1109\/ICCV51070.2023.00656"},{"key":"575_CR46","doi-asserted-by":"crossref","unstructured":"Patashnik O, Wu Z, Shechtman E, Cohen-Or D, Lischinski D (2021) Styleclip: Text-driven manipulation of stylegan imagery. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2065\u20132074","DOI":"10.1109\/ICCV48922.2021.00209"},{"key":"575_CR47","doi-asserted-by":"crossref","unstructured":"Pearson K (1896) Mathematical contributions to the theory of evolution.-on a form of spurious correlation which may arise when indices are used in the measurement of organs. In: Proceedings of the royal society of London, vol 60, pp 489\u2013498","DOI":"10.1098\/rspl.1896.0076"},{"key":"575_CR48","doi-asserted-by":"publisher","first-page":"5893","DOI":"10.1109\/TIP.2023.3326675","volume":"32","author":"M Pernu\u0161","year":"2023","unstructured":"Pernu\u0161 M, \u0160truc V, Dobri\u0161ek S (2023) Maskfacegan: High-resolution face editing with masked gan latent code optimization. IEEE Trans Image Process 32:5893\u20135908","journal-title":"IEEE Trans Image Process"},{"key":"575_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.127245","volume":"277","author":"F Ren","year":"2025","unstructured":"Ren F, Liu W, Wang F, Wang B, Sun F (2025) Facial attribute editing via a balanced simple attention generative adversarial network. Expert Syst Appl 277:127245","journal-title":"Expert Syst Appl"},{"key":"575_CR50","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. In: Proceedings of advances in neural information processing systems, pp 2234\u20132242"},{"key":"575_CR51","doi-asserted-by":"crossref","unstructured":"Shao M, Zhang Y, Liu H, Wang C, Li L, Shao X (2021) Dmdit: Diverse multi-domain image-to-image translation. Knowl-Based Syst 107311","DOI":"10.1016\/j.knosys.2021.107311"},{"key":"575_CR52","doi-asserted-by":"crossref","unstructured":"Shen Y, Gu J, Tang X, Zhou B (2020) 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","DOI":"10.1109\/CVPR42600.2020.00926"},{"key":"575_CR53","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1109\/TPAMI.2020.3034267","volume":"44","author":"Y Shen","year":"2020","unstructured":"Shen Y, Yang C, Tang X, Zhou B (2020) Interfacegan: Interpreting the disentangled face representation learned by gans. IEEE Trans Pattern Anal Mach Intell 44:2004\u20132018","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"575_CR54","doi-asserted-by":"crossref","unstructured":"Shen Y, Zhou B (2021) Closed-form factorization of latent semantics in gans. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1532\u20131540","DOI":"10.1109\/CVPR46437.2021.00158"},{"key":"575_CR55","doi-asserted-by":"crossref","unstructured":"Sun Z, Xiong S, Chen Y, Rong Y (2024) A fine rendering high-resolution makeup transfer network via inversion-editing strategy. Eng Appl Artif Intell 109346","DOI":"10.1016\/j.engappai.2024.109346"},{"key":"575_CR56","doi-asserted-by":"crossref","unstructured":"Suo J, Lin L, Shan S, Chen X, Gao W (2011) High-resolution face fusion for gender conversion. IEEE Trans Syst Man Cybern Part A Syst Humans 41:226\u2013237","DOI":"10.1109\/TSMCA.2010.2064304"},{"key":"575_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2018.03.045","volume":"309","author":"C Tian","year":"2018","unstructured":"Tian C, Li H, Gao X (2018) Photo-realistic 2d expression transfer based on fft and modified poisson image editing. Neurocomputing 309:1\u201310","journal-title":"Neurocomputing"},{"key":"575_CR58","doi-asserted-by":"crossref","unstructured":"Tian L, Yao H (2025) Retrievface: Retrieval-enhanced diffusion for controllable text-guided face editing. In: Proceedings of the 2025 international conference on multimedia retrieval, pp 1273\u20131282","DOI":"10.1145\/3731715.3733421"},{"key":"575_CR59","doi-asserted-by":"crossref","unstructured":"Torbunov D, Huang Y, Yu H, zhi Huang J, Yoo S, Lin M, Viren B, Ren Y (2022) Uvcgan: Unet vision transformer cycle-consistent gan for unpaired image-to-image translation. In: 2023 IEEE\/CVF winter conference on applications of computer vision, pp 702\u2013712","DOI":"10.1109\/WACV56688.2023.00077"},{"key":"575_CR60","first-page":"49","volume":"2020","author":"T Van","year":"2020","unstructured":"Van T, Nguyen T, Tran NN, Nguyen H, Doan L, Dao H, Minh T (2020) Interpreting the latent space of generative adversarial networks using supervised learning, in. Int Conf Adv Comput Appl 2020:49\u201354","journal-title":"Int Conf Adv Comput Appl"},{"key":"575_CR61","unstructured":"Voynov A, Babenko A (2020) Unsupervised discovery of interpretable directions in the gan latent space. In: Proceedings of international conference on machine learning, vol 119, pp 9786\u20139796"},{"key":"575_CR62","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"575_CR63","doi-asserted-by":"crossref","unstructured":"Wang X, Chen H, Tang S, Wu Z, Zhu W (2024) Disentangled representation learning. IEEE Trans Pattern Anal Mach Intell 9677\u20139696","DOI":"10.1109\/TPAMI.2024.3420937"},{"key":"575_CR64","unstructured":"Wang Q, Zhang J, Xu C, Cao W, Tai Y, Han Y, Ge Y, Gu H, Wang C,. Fu C (2024) Difffae: Advancing high-fidelity one-shot facial appearance editing with space-sensitive customization and semantic preservation. ArXiv:2403.17664"},{"key":"575_CR65","unstructured":"Wei M, Varanka T, Jiang X, Khor H-Q, Zhao G (2025) Magicface: High-fidelity facial expression editing with action-unit control. ArXiv:2501.02260"},{"key":"575_CR66","doi-asserted-by":"crossref","unstructured":"Yang M, Niu S, Wang Z, Li D, Du W (2023) Dfsgan: Introducing editable and representative attributes for few-shot image generation. Eng Appl Artif Intell 105519","DOI":"10.1016\/j.engappai.2022.105519"},{"key":"575_CR67","doi-asserted-by":"crossref","unstructured":"Yao X, Newson A, Gousseau Y, Hellier P (2021) A latent transformer for disentangled face editing in images and videos. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 13769\u201313778","DOI":"10.1109\/ICCV48922.2021.01353"},{"key":"575_CR68","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 586\u2013595","DOI":"10.1109\/CVPR.2018.00068"},{"key":"575_CR69","doi-asserted-by":"crossref","unstructured":"Zhang C, Liu S, Xu X, Zhu C (2019) C3ae: Exploring the limits of compact model for age estimation, in: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12587\u201312596","DOI":"10.1109\/CVPR.2019.01287"},{"key":"575_CR70","unstructured":"Zhuang P, Koyejo O, Schwing AG (2021) Enjoy your editing: controllable gans for image editing via latent space navigation. In: Proceedings of international conference on learning representations"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-026-00575-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00575-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00575-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:44:03Z","timestamp":1781196243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-026-00575-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,27]]},"references-count":70,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["575"],"URL":"https:\/\/doi.org\/10.1007\/s44443-026-00575-z","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,27]]},"assertion":[{"value":"1 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"173"}}