{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T11:01:57Z","timestamp":1780138917195,"version":"3.54.0"},"reference-count":61,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100018919","name":"Peng Cheng Laboratory","doi-asserted-by":"publisher","award":["PCL2025AS216"],"award-info":[{"award-number":["PCL2025AS216"]}],"id":[{"id":"10.13039\/100018919","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018919","name":"Peng Cheng Laboratory","doi-asserted-by":"publisher","award":["PCL2025AS17"],"award-info":[{"award-number":["PCL2025AS17"]}],"id":[{"id":"10.13039\/100018919","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YF A1008500"],"award-info":[{"award-number":["2023YF A1008500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22B2035"],"award-info":[{"award-number":["U22B2035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Graphics Forum"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    In the field of 3D content generation, single image scene reconstruction methods still struggle to simultaneously ensure the quality of individual assets and the coherence of the overall scene in complex environments, while texture editing techniques often fail to maintain both local continuity and multi\u2010view consistency. In this paper, we propose a novel system ZeroScene, which leverages the prior knowledge of large vision models to accomplish both single image\u2010to\u20103D scene reconstruction and texture editing in a zero\u2010shot manner. ZeroScene extracts object\u2010level 2D segmentation and depth information from input images to infer spatial relationships within the scene. It then jointly optimizes 3D and 2D projection losses of the point cloud to update object poses for precise scene alignment, ultimately constructing a coherent and complete 3D scene that encompasses both foreground and background. Moreover, ZeroScene supports texture editing of objects in the scene. By imposing constraints on the diffusion model and introducing a mask\u2010guided progressive image generation strategy, we effectively maintain texture consistency across multiple viewpoints and further enhance the realism of rendered results through Physically Based Rendering (PBR) material estimation. Experimental results demonstrate that our framework not only ensures the geometric and appearance accuracy of generated assets, but also faithfully reconstructs scene layouts and produces highly detailed textures that closely align with text prompts. Leveraging generative artificial intelligence, ZeroScene can transform 2D images into diversified 3D worlds with various styles, showing broad application potential in virtual content creation, such as digital twins and immersive game production, while also effectively supports \u201creal\u2010to\u2010sim\u201d transfer in robotics through the generation of highly realistic and trainable simulation environments. Project page:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/xdlbw.github.io\/ZeroScene\">https:\/\/xdlbw.github.io\/ZeroScene<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1111\/cgf.70419","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T10:14:36Z","timestamp":1776248076000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ZeroScene: A Zero\u2010Shot Framework for 3D Scene Generation from a Single Image and Controllable Texture Editing"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8931-4336","authenticated-orcid":false,"given":"X.","family":"Tang","sequence":"first","affiliation":[{"name":"HarbinInstitute of Technology  Shenzhen China"},{"name":"Pengcheng Laboratory  China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0882-8510","authenticated-orcid":false,"given":"R.","family":"Li","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory  China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9660-3636","authenticated-orcid":false,"given":"X.","family":"Fan","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory  China"},{"name":"Harbin Institute of Technology  China"},{"name":"Harbin Institute of Technology, Suzhou Research Institute  China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"e_1_2_8_2_2","doi-asserted-by":"crossref","unstructured":"ArdeleanA. \u00d6zerm. EggerB.: Generalizable 3d scene reconstruction via divide and conquer from a single view. InInternational Conference on 3D Vision (3DV)(2025). 2 3","DOI":"10.1109\/3DV66043.2025.00062"},{"key":"e_1_2_8_3_2","unstructured":"BatifolS. BlattmannA. BoeselF. ConsulS. DiagneC. DockhornT. EnglishJ. EnglishZ. EsserP. KulalS. et al.: Flux. 1 kontext: Flow matching for in-context image generation and editing in latent space.arXiv e-prints(2025) arXiv-2506. 2"},{"key":"e_1_2_8_4_2","unstructured":"BensadounR. KleimanY. AzuriI. HaroshO. VedaldiA. NeverovaN. GafniO.: Meta 3d texturegen: Fast and consistent texture generation for 3d objects.arXiv preprint arXiv:2407.02430(2024)."},{"key":"e_1_2_8_5_2","unstructured":"BinkowskiM. SutherlandD. J. ArbelM. GrettonA.: Demystifying MMD gans. In6th International Conference on Learning Representations(2018). 10"},{"key":"e_1_2_8_6_2","doi-asserted-by":"crossref","unstructured":"ChenC. HanZ. LiuY.-S. ZwickerM.: Unsupervised learning of fine structure generation for 3d point clouds by 2d projections matching. InProceedings of the ieee\/cvf international conference on computer vision(2021) pp.12466\u201312477. 5","DOI":"10.1109\/ICCV48922.2021.01224"},{"key":"e_1_2_8_7_2","doi-asserted-by":"crossref","unstructured":"ChenB. JiangH. LiuS. GuptaS. LiY. ZhaoH. WangS.: Physgen3d: Crafting a miniature interactive world from a single image. InProceedings of the Computer Vision and Pattern Recognition Conference(2025) pp.6178\u20136189. 2","DOI":"10.1109\/CVPR52734.2025.00579"},{"key":"e_1_2_8_8_2","doi-asserted-by":"crossref","unstructured":"ChengW. MuJ. ZengX. ChenX. PangA. ZhangC WangZ. FuB. YuG. LiuZ. et al.: Mvpaint: Synchronized multi-view diffusion for painting anything 3d. InProceedings of the Computer Vision and Pattern Recognition Conference(2025) pp.585\u2013594. 4 9 10","DOI":"10.1109\/CVPR52734.2025.00063"},{"key":"e_1_2_8_9_2","doi-asserted-by":"crossref","unstructured":"ChenD. Z. SiddiquiY. LeeH.-Y. TulyakovS. NiessnerM.: Text2tex: Text-driven texture synthesis via diffusion models. InProceedings of the IEEE\/CVF international conference on computer vision(2023) pp.18558\u201318568. 2 4","DOI":"10.1109\/ICCV51070.2023.01701"},{"key":"e_1_2_8_10_2","unstructured":"ChenM. WangJ. ShapovalovR. MonnierT. JungH. WangD. RanjanR. LainaI. VedaldiA.: Autopartgen: Autogressive 3d part generation and discovery.arXiv preprint arXiv:2507.13346(2025). 3"},{"key":"e_1_2_8_11_2","first-page":"128","volume-title":"European Conference on Computer Vision","author":"Chen Y.","year":"2024"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9811809"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1554"},{"key":"e_1_2_8_14_2","unstructured":"DaiT. WongJ. JiangY. WangC. GokmenC. ZhangR. WuJ. Fei-FeiL.: Automated creation of digital cousins for robust policy learning. In8th Annual Conference on Robot Learning(2024). 3"},{"key":"e_1_2_8_15_2","doi-asserted-by":"crossref","unstructured":"DongW. YangB. YangZ. LiY. Hu Bao H. MaY. CuiZ.: Hiscene: creating hierarchical 3d scenes with isometric view generation. InProceedings of the 33rd ACM International Conference on Multimedia(2025) pp.9783\u20139792. 3","DOI":"10.1145\/3746027.3755132"},{"key":"e_1_2_8_16_2","doi-asserted-by":"crossref","unstructured":"FuH. CaiB. GaoL. ZhangL.-X. WangJ. LiC. ZengQ. SunC JiaR. ZhaoB. et al.: 3d-front: 3d furnished rooms with layouts and semantics. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2021) pp.10933\u201310942. 7 8 14","DOI":"10.1109\/ICCV48922.2021.01075"},{"key":"e_1_2_8_17_2","doi-asserted-by":"crossref","unstructured":"FengY. YangM. YangS. ZhangS. YuJ. ZhaoZ. LiuY. JiangJ. GuoC.: Romantex: Decoupling 3d-aware rotary positional embedded multi-attentionnetwork for texture synthesis. InProceedings of the IEEE\/CVF international conference on computer vision(2025). 6 8","DOI":"10.1109\/ICCV51701.2025.01598"},{"key":"e_1_2_8_18_2","doi-asserted-by":"crossref","unstructured":"GuZ. CuiY. LiZ. WeiR GeY. GuJ. LiuM.-Y. DavisA. DingY.: Artiscene: Language-driven artistic 3d scene generation through image intermediary InProceedings of the Computer Vision and Pattern Recognition Conference(2025) pp.2891\u20132901. 3","DOI":"10.1109\/CVPR52734.2025.00275"},{"key":"e_1_2_8_19_2","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow I. J.","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"e_1_2_8_20_2","doi-asserted-by":"crossref","unstructured":"HuangZ. GuoY.-C. AnX. YangY. LiY. ZouZ.-X. LiangD. LiuX. CaoY.-P. ShengL.: Midi: Multi-instance diffusion for single image to 3d scene generation. InProceedings of the Computer Vision and Pattern Recognition Conference(2025) pp.23646\u201323657. 3 7 8","DOI":"10.1109\/CVPR52734.2025.02202"},{"key":"e_1_2_8_21_2","doi-asserted-by":"crossref","unstructured":"HuangZ. GuoY.-C. WangH. YiR. MaL. CaoY.-P. ShengL.: Mv-adapter: Multi-view consistent image generation made easy. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2025) pp.16377\u201316387. 4 9 10","DOI":"10.1109\/ICCV51701.2025.01520"},{"key":"e_1_2_8_22_2","first-page":"352","volume-title":"European Conference on Computer Vision","author":"Huo D.","year":"2024"},{"key":"e_1_2_8_23_2","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho J.","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"e_1_2_8_24_2","unstructured":"HurstA. LererA. GoucherA. P. PerelmanA. RameshA. ClarkA. OstrowA. WelihindaA. HayesA. RadfordA. et al.: Gpt-4o system card.arXiv preprint arXiv:2410.21276(2024). 3 4 5 8"},{"key":"e_1_2_8_25_2","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","volume":"30","author":"Heusel M.","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"e_1_2_8_26_2","doi-asserted-by":"crossref","unstructured":"HuangX. WangT. LiuZ. WangQ.: Material anything: Generating materials for any 3d object via diffusion. InProceedings of the Computer Vision and Pattern Recognition Conference(2025) pp.26556\u201326565. 4","DOI":"10.1109\/CVPR52734.2025.02473"},{"key":"e_1_2_8_27_2","doi-asserted-by":"crossref","unstructured":"HanH YangR. LiaoH XingJ. XuZ. YuX. ZhaJ. LiX. LiW.: Reparo: Compositional 3d assets generation with differentiable 3d layout alignment. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2025) pp.25367\u201325377. 2 3","DOI":"10.1109\/ICCV51701.2025.02353"},{"key":"e_1_2_8_28_2","unstructured":"HeZ. YangM. YangS. TangY. WangT. ZhangK. ChenG. LiuY. JiangJ. GuoC. et al.: Materialmvp: Illumination-invariant material generation via multi-view pbr diffusion. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2025). 4 6 8"},{"key":"e_1_2_8_29_2","unstructured":"KirillovA. MintunE. RaviN. MaoH. RollandC. GustafsonL. XiaoT. WhiteheadS. BergA. C. LoW.-Y. et al.: Segment anything. InProceedings of the IEEE\/CVF international conference on computer vision(2023) pp.4015\u20134026. 3"},{"key":"e_1_2_8_30_2","doi-asserted-by":"crossref","unstructured":"LiuY. XieM. LiuH. WongT.-T.: Text-guided texturing by synchronized multi-view diffusion. InSIGGRAPH Asia 2024 Conference Papers(2024) pp.1\u201311. 2","DOI":"10.1145\/3680528.3687621"},{"key":"e_1_2_8_31_2","unstructured":"LaiZ. ZhaoY. LiuH. ZhaoZ. LinQ. ShiH. YangX. YangM. YangS. FengY. et al.: Hunyuan3d 2.5: Towards high-fidelity 3d assets generation with ultimate details.arXiv preprint arXiv:2506.16504(2025). 3 4 7 8"},{"key":"e_1_2_8_32_2","doi-asserted-by":"crossref","unstructured":"LiY. ZouZ.-X. LiuZ. WangD. LiangY. YuZ. LiuX. GuoY.-C LiangD. OuyangW. et al.: Triposg: High-fidelity 3d shape synthesis using large-scale rectified flow models.arXiv preprint arXiv:2502.06608(2025). 2","DOI":"10.1109\/TPAMI.2025.3633512"},{"key":"e_1_2_8_33_2","first-page":"38","volume-title":"European conference on computer vision","author":"Liu S.","year":"2024"},{"key":"e_1_2_8_34_2","doi-asserted-by":"crossref","unstructured":"LiuJ.-H. ZhangS.-K. ZhangC. ZhangS.-H.: Controllable procedural generation of landscapes. InProceedings of the 32nd ACM International Conference on Multimedia(2024) pp.6394\u20136403. 3","DOI":"10.1145\/3664647.3681129"},{"key":"e_1_2_8_35_2","doi-asserted-by":"crossref","unstructured":"MengY. WuH. ZhangY. XieW.: Scenegen: Single-image 3d scene generation in one feedforward pass. In2026 International Conference on 3D Vision (3DV)(2026). 3 7 8","DOI":"10.1109\/3DV69130.2026.00058"},{"key":"e_1_2_8_36_2","unstructured":"OquabM. DarcetT. MoutakanniT. VoH. SzafraniecM. KhalidovV. FernandezR. HazizaD. MassaF. El-NoubyA. et al.: Dinov2: Learning robust visual features without supervision.Transactions on Machine Learning Research Journal(2024). 9"},{"key":"e_1_2_8_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3658222"},{"key":"e_1_2_8_38_2","unstructured":"RombachR. BlattmannA. LorenzD. EsserR. OmmerB.: High-resolutionimage synthesis with latent diffusion models. InProceedings of the IEEE\/CVF conference on computer vision and pattern recognition(2022) pp.10684\u201310695. 2 3"},{"key":"e_1_2_8_39_2","first-page":"8748","volume-title":"International conference on machine learning","author":"Radford A.","year":"2021"},{"key":"e_1_2_8_40_2","unstructured":"RenT. LiuS. ZengA. LinJ. LiK. CaoH. ChenI. HuangX. ChenY. YanF. et al.: Grounded sam: Assembling open-world models for diverse visual tasks.arXiv preprint arXiv:2401.14159(2024). 3 4"},{"key":"e_1_2_8_41_2","doi-asserted-by":"crossref","unstructured":"RichardsonE. MetzerG. AlalufY. GiryesR. Cohen-OrD.: Texture: Text-guided texturing of 3d shapes. InACM SIGGRAPH 2023 conference proceedings(2023) pp.1\u201311. 2 3 4 6 9 10","DOI":"10.1145\/3588432.3591503"},{"key":"e_1_2_8_42_2","first-page":"36479","article-title":"Photorealistic text-to-image diffusion models with deep language understanding","volume":"35","author":"Saharia C","year":"2022","journal-title":"Advances in neural information processing systems"},{"key":"e_1_2_8_43_2","unstructured":"SuvorovR. LogachevaE. MashikhinA. RemizovaA. AshukhaA. SilvestrovA. KongN. GokaH. ParkK. LempitskyV.: Resolution-robust large mask inpainting with fourier convolutions. InProceedings of the IEEE\/CVF winter conference on applications of computer vision(2022) pp.2149\u20132159. 3"},{"key":"e_1_2_8_44_2","unstructured":"TeamK.: Kolors: Effective training of diffusion model for photorealistic text-to-image synthesis.arXiv preprint(2024). 2 6"},{"key":"e_1_2_8_45_2","unstructured":"TeamT. H.:Hunyuan3d 2.0: Scaling diffusion models for high resolution textured 3d assets generation 2025. 7"},{"key":"e_1_2_8_46_2","doi-asserted-by":"crossref","unstructured":"WuQ. IliashD. RitchieD. SavvaM. ChangA. X.: Diorama: Unleashing zero-shot single-view 3d indoor scene modeling. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2025) pp.8896\u20138907. 3","DOI":"10.1109\/ICCV51701.2025.00832"},{"key":"e_1_2_8_47_2","doi-asserted-by":"crossref","unstructured":"WangS. LeroyV. CabonY. ChidlovskiiB. RevaudJ.: Dust3r: Geometric 3d vision made easy. InProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(2024) pp.20697\u201320709. 3 4 5","DOI":"10.1109\/CVPR52733.2024.01956"},{"key":"e_1_2_8_48_2","first-page":"125116","article-title":"Unique3d: High-quality and efficient 3d mesh generation from a single image","volume":"37","author":"Wu K.","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_8_49_2","unstructured":"WangX. LiuL. CaoY. WuR. QinW. WangD. SuiW. SuZ.: Embodiedgen: Towards a generative 3d world engine for embodied intelligence.arXiv preprint arXiv:2506.10600(2025). 2 3 9 10"},{"key":"e_1_2_8_50_2","unstructured":"WuS. LinY. ZhangF. ZengY. YangY. BaoY. QianJ. ZhuS. CaoX. TorrP. et al.: Direct3d-s2: Gigascale 3d generation made easy with spatial sparse attention.Advances in Neural Information Processing Systems(2025). 3"},{"key":"e_1_2_8_51_2","doi-asserted-by":"crossref","unstructured":"WangX. XieL. DongC. ShanY.: Real-esrgan: Training real-world blind super-resolution with pure synthetic data. InProceedings of the IEEE\/CVF international conference on computer vision(2021) pp.1905\u20131914. 7","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"e_1_2_8_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV61041.2025.00477"},{"key":"e_1_2_8_53_2","doi-asserted-by":"crossref","unstructured":"XiangJ. LvZ. XuS. DengY. WangR. ZhangB. ChenD. TongX. YangJ.: Structured 3d latents for scalable and versatile 3d generation. InProceedings of the Computer Vision and Pattern Recognition Conference(2025) pp.21469\u201321480. 2 8","DOI":"10.1109\/CVPR52734.2025.02000"},{"key":"e_1_2_8_54_2","doi-asserted-by":"crossref","unstructured":"YouwangK. OhT.-H. Pons-MollG.: Paint-it: Text-to-texture synthesis via deep convolutional texture map optimization and physically-based rendering. InProceedings of the ieee\/cvf conference on computer vision and pattern recognition(2024) pp.4347\u20134356. 4","DOI":"10.1109\/CVPR52733.2024.00416"},{"key":"e_1_2_8_55_2","doi-asserted-by":"crossref","unstructured":"YangJ. SaxA. LiangK. J. HenaffM. TangH. CaoA. ChaiJ. MeierF. FeiszliM.: Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass. InProceedings of the Computer Vision and Pattern Recognition Conference(2025) pp.21924\u201321935. 3","DOI":"10.1109\/CVPR52734.2025.02042"},{"key":"e_1_2_8_56_2","doi-asserted-by":"crossref","unstructured":"YeC. WuY. LuZ. ChangJ. GuoX. ZhouJ. ZhaoH. HanX.: Hi3dgen: High-fidelity 3d geometry generation from images via normal bridging. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2025). 3","DOI":"10.1109\/ICCV51701.2025.02323"},{"key":"e_1_2_8_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3730841"},{"key":"e_1_2_8_58_2","doi-asserted-by":"crossref","unstructured":"ZengX. ChenX. QiZ. LiuW. ZhaoZ. WangZ. FuB. LiuY. YuG.: Paint3d: Paint anything 3d with lighting-less texture diffusion models. InProceedings of the IEEE\/CVF conference on computer vision and pattern recognition(2024) pp.4252\u20134262. 4","DOI":"10.1109\/CVPR52733.2024.00407"},{"key":"e_1_2_8_59_2","first-page":"39104","article-title":"Zero-shot scene reconstruction from single images with deep prior assembly","volume":"37","author":"Zhou J.","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_8_60_2","doi-asserted-by":"crossref","unstructured":"ZhangS. PengS. XuT. YangY. ChenT. XueN. ShenY. BaoH. HuR. ZhouX.: Mapa: Text-driven photorealistic material painting for 3d shapes. InACM SIGGRAPH 2024 Conference Papers(2024) pp.1\u201312. 4","DOI":"10.1145\/3641519.3657504"},{"key":"e_1_2_8_61_2","unstructured":"ZhangL. RaoA. AgrawalaM.: Adding conditional control to text-to-image diffusion models. InProceedings of the IEEE\/CVF international conference on computer vision(2023) pp.3836\u20133847. 3 6"},{"key":"e_1_2_8_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3658146"}],"container-title":["Computer Graphics Forum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/cgf.70419","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/cgf.70419","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/cgf.70419","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T10:27:27Z","timestamp":1780136847000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/cgf.70419"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,15]]},"references-count":61,"alternative-id":["10.1111\/cgf.70419"],"URL":"https:\/\/doi.org\/10.1111\/cgf.70419","archive":["Portico"],"relation":{},"ISSN":["0167-7055","1467-8659"],"issn-type":[{"value":"0167-7055","type":"print"},{"value":"1467-8659","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,15]]},"assertion":[{"value":"2026-04-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70419"}}