{"id":10237,"date":"2026-08-19T21:40:01","date_gmt":"2026-08-19T13:40:01","guid":{"rendered":"\/jase\/?post_type=tkuisotope&#038;p=10237"},"modified":"2026-08-19T23:07:42","modified_gmt":"2026-08-19T15:07:42","slug":"jase-202611-34-056","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-056","title":{"rendered":"Graphcut-Based Animation Texture Synthesis with Temporal Consistency Constraints"},"content":{"rendered":"\n<div class=\"wp-block-tkuwpbs5-bs5-row row article-info\">\n<div class=\"wp-block-tkuwpbs5-bs5-column col-md-3 align-self-start\">\n<p><i class=\"fa fa-folder\" aria-hidden=\"true\"><\/i>&nbsp;<a href=\"\/jase\/?page_id=807\" data-type=\"page\" data-id=\"807\">2026<\/a><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-tkuwpbs5-bs5-column col-md-3 align-self-start\">\n<p><i class=\"fa fa-folder-open\" aria-hidden=\"true\"><\/i>&nbsp;<a href=\"\/jase\/?page_id=9439\" data-type=\"page\" data-id=\"9439\">Volume 34<\/a><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-tkuwpbs5-bs5-column col-md-6 align-self-start\">\n<div class=\"wp-block-tkuwpbs5-bs5-div dv_publish\" data-aos=\"normal\"><div class=\"wp-block-post-date\"><time datetime=\"2026-08-19T21:40:01+08:00\">2026-08-19<\/time><\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-tkuwpbs5-bs5-row row\">\n<div class=\"wp-block-tkuwpbs5-bs5-column col-md-5 align-self-start\">\n<div class=\"wp-block-tkuwpbs5-bs5-div au-ol\" data-aos=\"normal\">\n<p>Shuang Cui<sup>1<\/sup><a href=\"mailto:peter15119576@163.com\"><i class=\"fa fa-envelope\"><\/i><\/a> and Chunxin Zhang<sup>2<\/sup><\/p>\n\n\n\n<p style=\"font-size:14px\"><sup>1<\/sup>School of Hunan Mass Media Vocational and Technical College; Changsha Hunan 410000 China<\/p>\n\n\n\n<p style=\"font-size:14px\"><sup>2<\/sup>School of Informatics, Xiamen University, Xiamen, P.R. China<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-tkuwpbs5-bs5-div\" style=\"margin-top:var(--wp--preset--spacing--40)\" data-aos=\"normal\">\n<p>Received: May 13, 2026<br>Accepted:&nbsp;July 28, 2026<br>Publication Date:&nbsp;August 19, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-tkuwpbs5-bs5-column col-md-7 align-self-start clk=\u5716\u7247\"><img decoding=\"async\" src=\"\/jase\/wp-content\/uploads\/2026\/08\/34_056.jpg\" class=\"img-fluid img-fluid mx-auto d-block\" alt=\"\u4e0a\u50b3\u5716\u7247\">\n\n\n<p class=\"has-text-align-center\">Temporal Differential Error (TDE) of different methods in animation sequences.<\/p>\n<\/div>\n<\/div>\n\n\n\n<p class=\"has-small-font-size\"><i class=\"fab fa-creative-commons\"><\/i>&nbsp;<strong>Copyright&nbsp;<\/strong>The Author(s). This is an open access article distributed under the terms of the&nbsp;<a rel=\"noreferrer noopener\" href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\" target=\"_blank\">Creative Commons Attribution&nbsp;License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n<p>Download Citation:\u00a0 <a href=\"\/jase\/wp-content\/uploads\/2026\/08\/V34.0056.txt\" data-type=\"attachment\" data-id=\"9812\" target=\"_blank\" rel=\"noreferrer noopener\">BibTeX <\/a>| <a rel=\"noreferrer noopener\" href=\"http:\/\/dx.doi.org\/10.6180\/jase.202611_34.056\" target=\"_blank\">http:\/\/dx.doi.org\/10.6180\/jase.202611_34.056<\/a>\u00a0\u00a0<\/p>\n\n\n\n<p class=\"btn btn-primary article-btn\"><a href=\"\/jase\/wp-content\/uploads\/2026\/08\/056_2026_1281_V34.pdf\" data-type=\"attachment\" data-id=\"10251\" target=\"_blank\" rel=\"noreferrer noopener\">Download PDF<\/a><\/p>\n\n\n\n<div style=\"height:24px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Animated texture replacement is an important research problem in computer graphics and digital content generation. Its core challenge lies in simultaneously ensuring spatial continuity, temporal consistency, and lighting stability during texture stitching. Traditional texture synthesis methods are primarily designed for static images, and their direct application to animation sequences often leads to seam jitter, texture flickering, and lighting drift, thereby degrading visual quality. To address these limitations, this paper proposes a Graphcut based animated texture replacement method. Within a block-level texture synthesis framework, the proposed approach constructs a pixel-level energy function and employs a Maximum Flow\u2013Minimum Cut model to determine globally optimal seams in overlapping regions, thereby ensuring spatial consistency. Furthermore, temporal consistency constraints are introduced to suppress inter-frame texture fluctuations, while a customized lighting model maintains stable and controllable illumination throughout continuous animation sequences. To accelerate the computationally intensive block matching stage, CUDA-based parallel processing is incorporated, significantly improving execution efficiency. Experimental results demonstrate that the proposed method achieves an average Temporal Difference Error (TDE) of approximately 2.3, representing about a 61% reduction compared with the best-performing baseline method, while also achieving the lowest Temporal Illumination Variance (TIV), indicating superior temporal stability and illumination consistency. Overall, the proposed framework provides high-quality texture synthesis with improved computational performance, making it suitable for texture replacement tasks in complex animation scenes.<\/p>\n\n\n\n<p><em>Keywords:&nbsp;Animated texture replacement; Graphcut algorithm; Temporal consistency; Lighting customization; Texture composition; CUDA parallel acceleration<\/em><\/p>\n\n\n\n<div style=\"height:2rem\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-tkuwpbs5-bs5-div ref_ol\" data-aos=\"normal\">\n<div class=\"container\">\n<div id=\"model-response-message-contentr_53180f64958e6cc2\" class=\"markdown markdown-main-panel md-content enable-luminous-fast-follows enable-updated-hr-color stronger\" dir=\"ltr\" aria-busy=\"false\" aria-live=\"polite\">\n<ol>\n<li data-path-to-node=\"0\">[1] K. Zhang, D. Zhu, X. Min, and G. Zhai. \u201cTextured Mesh Saliency: Bridging Geometry and Texture for Human Perception in 3D Graphics\u201d. In: Proceedings of the AAAI Conference on Artificial Intelligence. 39. 9. 2025, 9977\u20139984. DOI: 10.1609\/aaai.v39i9.33082.<\/li>\n<li data-path-to-node=\"0\">[2] P. Tu, L.-Y. Wei, and M. Zwicker. \u201cCompositional Neural Textures\u201d. In: SIGGRAPH Asia 2024 Conference Papers. New York: ACM, 2024, 1\u201311. DOI: 10.1145\/3680528.3687561.<\/li>\n<li data-path-to-node=\"0\">[3] H. Wu, Z. Wang, Y. Li, X. Liu, and T.-Y. Lee, (2024) \u201cSuitable and Style-Consistent Multi-Texture Recommendation for Cartoon Illustrations\u201d ACM Transactions on Multimedia Computing, Communications and Applications 20(7): 1\u201326. DOI: 10.1145\/3652518.<\/li>\n<li data-path-to-node=\"0\">[4] Y. Zeng, L. Ma, and P. V. Sander. \u201cGSWT: Gaussian Splatting Wang Tiles\u201d. In: SIGGRAPH Asia 2025 Conference Papers. New York: ACM, 2025, 1\u201311. DOI: 10.1145\/3757377.3763967.<\/li>\n<li data-path-to-node=\"0\">[5] X. Liang, W. Yao, X. Fang, and C. Zhang, (2025) \u201cFMIF: Facial Multi-Feature Information Fusion for Driver Fatigue Detection\u201d Signal, Image and Video Processing 19(2): Article 121. DOI: 10.1007\/s11760-024-03573-8.<\/li>\n<li data-path-to-node=\"0\">[6] Z. Ou, X. Liu, C. Li, Z. Wen, P. Li, Z. Gao, and H. Wu, (2024) \u201cBody Part Segmentation of Anime Characters\u201d Computer Animation and Virtual Worlds 35(6): e2295. DOI: 10.1002\/cav.2295.<\/li>\n<li data-path-to-node=\"0\">[7] T. Elsner, J. Berger, T. Wu, V. Czech, L. Gao, and L. Kobbelt. \u201cRetargeting Visual Data with Deformation Fields\u201d. In: European Conference on Computer Vision. Cham: Springer, 2024, 271\u2013288. DOI: 10.1007\/978-3-031-72949-2_16.<\/li>\n<li data-path-to-node=\"0\">[8] W. Gong and Q. Hou, (2026) \u201cAdvanced Techniques in Digital Media Processing for Special Effects Enhancement in Film and Television Post-Production\u201d Multimedia Systems 32(1): 17. DOI: 10.1007\/s00530-025-02077-w.<\/li>\n<li data-path-to-node=\"0\">[9] I. Oliveira, T. Pinto, S. Afonso, M. Kara\u015b, U. Szymanowska, B. Gon\u00e7alves, and A. Vilela, (2025) \u201cSustainability in Bio-Based Edible Films, Coatings, and Packaging for Small Fruits\u201d Applied Sciences 15(3): 1462. DOI: 10.3390\/app15031462.<\/li>\n<li data-path-to-node=\"0\">[10] M. Vijendran, J. Deng, S. Chen, E. S. L. Ho, and H. P. H. Shum, (2024) \u201cArtificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey\u201d Artificial Intelligence Review 58(2): Article 64. DOI: 10.48550\/arXiv.2412.01450.<\/li>\n<li data-path-to-node=\"0\">[11] J. Ma, E. Lu, R. Paiss, S. Zada, A. Holynski, T. Dekel, and F. Cole. \u201cVidPanos: Generative Panoramic Videos from Casual Panning Videos\u201d. In: SIGGRAPH Asia 2024 Conference Papers. New York: ACM, 2024, 1\u201311. DOI: 10.1145\/3680528.3687664.<\/li>\n<li data-path-to-node=\"0\">[12] M. Xu, X. Tao, J. Liu, L. Pan, and Z. Huang, (2025) \u201cResearch on the Innovation of Rattan Weaving Art Design Form and Inheritance of Cultural Symbols of Maonan Flower Bamboo Hat Based on Complex Geometry and AI Algorithm\u201d Journal of Combinatorial Mathematics and Combinatorial Computing 127: 9365\u20139383. DOI: 10.61091\/jcmcc127a-522.<\/li>\n<li data-path-to-node=\"0\">[13] G. Zou, J. Jiang, and Q. Chen, (2024) &#8220;Accelerated Reconstruction of Scenes Using CUDA-Based Parallel Computing&#8221; IEEE Access 13: 10489\u201310498. DOI: 10.1109\/ACCESS.2024.3523099.<\/li>\n<li data-path-to-node=\"0\">[14] M. Li, Z. Bi, T. Wang, Y. Wen, Q. Niu, X. Song, and M. Liu, (2024) &#8220;Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing&#8221; arXiv: DOI: 10.48550\/arXiv.2410.05686.<\/li>\n<li data-path-to-node=\"0\">[15] G. Mellone, C. G. De Vita, E. Di Nardo, G. Coviello, D. Di Luccio, P. P. C. Aucelli, and R. Montella. &#8220;G-Litter Marine Litter Dataset Augmentation with Diffusion Models and Large Language Models on GPU Acceleration&#8221;. In: 2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). 2025, 526\u2013535. DOI: 10.1109\/PDP66500.2025.00081.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<p><\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,1682,6],"tags":[1738],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":"&nbsp;Copyright&nbsp;The Author(s). This is an open access article distributed under the terms of the&nbsp;Creative Commons Attribution&nbsp;License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:\u00a0 BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202611_34.056\u00a0\u00a0 Download PDF Animated texture replacement is an important research problem in computer graphics&hellip;","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/10237"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10237"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10237"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10237"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}