{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T16:17:34Z","timestamp":1771604254020,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology","award":["2024B1212010006"],"award-info":[{"award-number":["2024B1212010006"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s11263-025-02629-7","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T06:46:44Z","timestamp":1767768404000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["OmniDrag: Enabling Motion Control for Omnidirectional Image-to-Video Generation"],"prefix":"10.1007","volume":"134","author":[{"given":"Weiqi","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong","family":"Mou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuhan","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junlin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-3125","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"2629_CR1","doi-asserted-by":"crossref","unstructured":"Xiao, J., Ehinger, K.A., Oliva, A., & Torralba, A.(2012). Recognizing scene viewpoint using panoramic place representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2695\u20132702. IEEE","DOI":"10.1109\/CVPR.2012.6247991"},{"issue":"4","key":"2629_CR2","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1109\/JPROC.2019.2894817","volume":"107","author":"M Zink","year":"2019","unstructured":"Zink, M., Sitaraman, R., & Nahrstedt, K. (2019). Scalable 360 video stream delivery: Challenges, solutions, and opportunities. Proceedings of the IEEE, 107(4), 639\u2013650.","journal-title":"Proceedings of the IEEE"},{"key":"2629_CR3","doi-asserted-by":"crossref","unstructured":"Ai, H., Cao, Z., & Wang, L.(2025). A survey of representation learning, optimization strategies, and applications for omnidirectional vision. International Journal of Computer Vision (IJCV)","DOI":"10.1007\/s11263-025-02391-w"},{"key":"2629_CR4","doi-asserted-by":"crossref","unstructured":"Esser, P., Chiu, J., Atighehchian, P., Granskog, J., & Germanidis, A.(2023). Structure and content-guided video synthesis with diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 7346\u20137356","DOI":"10.1109\/ICCV51070.2023.00675"},{"key":"2629_CR5","unstructured":"Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., & Letts, A., et al.(2023). Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv:2311.15127"},{"key":"2629_CR6","unstructured":"Brooks, T., Peebles, B., Holmes, C., DePue, W., Guo, Y., Jing, L., Schnurr, D., Taylor, J., Luhman, T., Luhman, E., Ng, C., Wang, R., & Ramesh, A.(2024). Video generation models as world simulators. https:\/\/openai.com\/research\/video-generation-models-as-world-simulators"},{"key":"2629_CR7","doi-asserted-by":"crossref","unstructured":"Wang, Q., Li, W., Mou, C., Cheng, X., & Zhang, J.(2024). 360dvd: Controllable panorama video generation with 360-degree video diffusion model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR52733.2024.00660"},{"issue":"5","key":"2629_CR8","doi-asserted-by":"publisher","first-page":"5570","DOI":"10.1109\/TITS.2023.3241212","volume":"24","author":"H Shi","year":"2023","unstructured":"Shi, H., Zhou, Y., Yang, K., Yin, X., Wang, Z., Ye, Y., Yin, Z., Meng, S., Li, P., & Wang, K. (2023). Panoflow: Learning 360$$^{\\circ }$$ optical flow for surrounding temporal understanding. IEEE Transactions on Intelligent Transportation Systems (TITS), 24(5), 5570\u20135585.","journal-title":"IEEE Transactions on Intelligent Transportation Systems (TITS)"},{"key":"2629_CR9","doi-asserted-by":"crossref","unstructured":"Hao, Z., Huang, X., & Belongie, S.(2018). Controllable video generation with sparse trajectories. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7854\u20137863","DOI":"10.1109\/CVPR.2018.00819"},{"key":"2629_CR10","unstructured":"Yin, S., Wu, C., Liang, J., Shi, J., Li, H., Ming, G., & Duan, N.(2023). Dragnuwa: Fine-grained control in video generation by integrating text, image, and trajectory. arXiv:2308.08089"},{"key":"2629_CR11","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yuan, Z., Wang, X., Li, Y., Chen, T., Xia, M., Luo, P., & Shan, Y.(2024). Motionctrl: A unified and flexible motion controller for video generation. In: ACM SIGGRAPH 2024 Conference Papers, pp. 1\u201311","DOI":"10.1145\/3641519.3657518"},{"key":"2629_CR12","doi-asserted-by":"crossref","unstructured":"Wu, W., Li, Z., Gu, Y., Zhao, R., He, Y., Zhang, D.J., Shou, M.Z., Li, Y., Gao, T., & Zhang, D. (2025) Draganything: Motion control for anything using entity representation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 331\u2013348 . Springer","DOI":"10.1007\/978-3-031-72670-5_19"},{"issue":"2","key":"2629_CR13","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1086\/427976","volume":"622","author":"KM Gorski","year":"2005","unstructured":"Gorski, K. M., Hivon, E., Banday, A. J., Wandelt, B. D., Hansen, F. K., Reinecke, M., & Bartelmann, M. (2005). Healpix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere. The Astrophysical Journal, 622(2), 759.","journal-title":"The Astrophysical Journal"},{"key":"2629_CR14","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 33, 6840\u20136851.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2629_CR15","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., & Nichol, A. (2021). Diffusion models beat gans on image synthesis. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 34, 8780\u20138794.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2629_CR16","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 (CVPR), pp. 10684\u201310695","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"2629_CR17","first-page":"36479","volume":"35","author":"C Saharia","year":"2022","unstructured":"Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., & Salimans, T. (2022). Photorealistic text-to-image diffusion models with deep language understanding. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 35, 36479\u201336494.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2629_CR18","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M.(2022). Hierarchical text-conditional image generation with clip latents. arXiv:2204.06125 1(2), 3"},{"key":"2629_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., & Agrawala, M.(2023). Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3836\u20133847","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"2629_CR20","doi-asserted-by":"crossref","unstructured":"Mou, C., Wang, X., Xie, L., Wu, Y., Zhang, J., Qi, Z., & Shan, Y.(2024). T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 38, pp. 4296\u20134304","DOI":"10.1609\/aaai.v38i5.28226"},{"key":"2629_CR21","doi-asserted-by":"crossref","unstructured":"Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S.W., Fidler, S., & Kreis, K.(2023). Align your latents: High-resolution video synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22563\u201322575","DOI":"10.1109\/CVPR52729.2023.02161"},{"key":"2629_CR22","unstructured":"Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D.P., Poole, B., Norouzi, M., & Fleet, D.J., et al.(2022). Imagen video: High definition video generation with diffusion models. arXiv:2210.02303"},{"key":"2629_CR23","unstructured":"Guo, Y., Yang, C., Rao, A., Liang, Z., Wang, Y., Qiao, Y., Agrawala, M., Lin, D., & Dai, B.(2024). Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"2629_CR24","doi-asserted-by":"crossref","unstructured":"Xing, J., Xia, M., Zhang, Y., Chen, H., Wang, X., Wong, T.-T., & Shan, Y.(2023). Dynamicrafter: Animating open-domain images with video diffusion priors. Proceedings of the European Conference on Computer Vision (ECCV)","DOI":"10.1007\/978-3-031-72952-2_23"},{"key":"2629_CR25","unstructured":"Zhang, S., Wang, J., Zhang, Y., Zhao, K., Yuan, H., Qin, Z., Wang, X., Zhao, D., & Zhou, J.(2023). I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models. arXiv:2311.04145"},{"key":"2629_CR26","unstructured":"Chen, W., Ji, Y., Wu, J., Wu, H., Xie, P., Li, J., Xia, X., Xiao, X., & Lin, L.(2023). Control-a-video: Controllable text-to-video generation with diffusion models. arXiv:2305.13840"},{"key":"2629_CR27","unstructured":"Zhang, Y., Wei, Y., Jiang, D., Zhang, X., Zuo, W., & Tian, Q.(2023). Controlvideo: Training-free controllable text-to-video generation. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"2629_CR28","unstructured":"Peng, B., Wang, J., Zhang, Y., Li, W., Yang, M.-C., & Jia, J.(2024). Controlnext: Powerful and efficient control for image and video generation. arXiv:2408.06070"},{"key":"2629_CR29","unstructured":"Mou, C., Wang, X., Song, J., Shan, Y., & Zhang, J.(2024). Dragondiffusion: Enabling drag-style manipulation on diffusion models. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"2629_CR30","doi-asserted-by":"crossref","unstructured":"Mou, C., Wang, X., Song, J., Shan, Y., & Zhang, J.(2023). Diffeditor: Boosting accuracy and flexibility on diffusion-based image editing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR52733.2024.00811"},{"key":"2629_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liao, J., Li, M., Qin, L., & Wang, W.(2024). Tora: Trajectory-oriented diffusion transformer for video generation. arXiv:2407.21705","DOI":"10.1109\/CVPR52734.2025.00198"},{"key":"2629_CR32","doi-asserted-by":"crossref","unstructured":"Ma, W.-D.K., Lewis, J.P., & Kleijn, W.B.(2023). Trailblazer: Trajectory control for diffusion-based video generation. arXiv:2401.00896","DOI":"10.1145\/3680528.3687652"},{"key":"2629_CR33","doi-asserted-by":"crossref","unstructured":"Jain, Y., Nasery, A., Vineet, V., & Behl, H.(2024). Peekaboo: Interactive video generation via masked-diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8079\u20138088","DOI":"10.1109\/CVPR52733.2024.00772"},{"key":"2629_CR34","unstructured":"Wang, J., Zhang, Y., Zou, J., Zeng, Y., Wei, G., Yuan, L., & Li, H.(2024). Boximator: Generating rich and controllable motions for video synthesis. arXiv:2402.01566"},{"key":"2629_CR35","unstructured":"Qiu, H., Chen, Z., Wang, Z., He, Y., Xia, M., & Liu, Z.(2024). Freetraj: Tuning-free trajectory control in video diffusion models. arXiv:2406.16863"},{"key":"2629_CR36","doi-asserted-by":"crossref","unstructured":"Oh, C., Cho, W., Chae, Y., Park, D., Wang, L., & Yoon, K.-J.(2022). Bips: Bi-modal indoor panorama synthesis via residual depth-aided adversarial learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 352\u2013371. Springer","DOI":"10.1007\/978-3-031-19787-1_20"},{"key":"2629_CR37","unstructured":"Teterwak, P., Sarna, A., Krishnan, D., Maschinot, A., Belanger, D., Liu, C., & Freeman, W.T.(2019). Boundless: Generative adversarial networks for image extension. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10521\u201310530"},{"key":"2629_CR38","doi-asserted-by":"crossref","unstructured":"Wu, S., Tang, H., Jing, X.-Y., Zhao, H., Qian, J., Sebe, N., & Yan, Y.(2022). Cross-view panorama image synthesis. IEEE Transactions on Multimedia (TMM)","DOI":"10.1016\/j.patcog.2022.108884"},{"key":"2629_CR39","doi-asserted-by":"crossref","unstructured":"Lin, C.H., Chang, C.-C., Chen, Y.-S., Juan, D.-C., Wei, W., & Chen, H.-T.(2019). Coco-gan: Generation by parts via conditional coordinating. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4512\u20134521","DOI":"10.1109\/ICCV.2019.00461"},{"key":"2629_CR40","unstructured":"Lin, C.H., Lee, H.-Y., Cheng, Y.-C., Tulyakov, S., & Yang, M.-H.(2022). Infinitygan: Towards infinite-pixel image synthesis. Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"2629_CR41","doi-asserted-by":"crossref","unstructured":"Cheng, Y.-C., Lin, C.H., Lee, H.-Y., Ren, J., Tulyakov, S., & Yang, M.-H.(2022). Inout: Diverse image outpainting via gan inversion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11431\u201311440","DOI":"10.1109\/CVPR52688.2022.01114"},{"key":"2629_CR42","doi-asserted-by":"crossref","unstructured":"Wang, G., Yang, Y., Loy, C.C., & Liu, Z.(2022). Stylelight: Hdr panorama generation for lighting estimation and editing. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 477\u2013492 . Springer","DOI":"10.1007\/978-3-031-19784-0_28"},{"issue":"6","key":"2629_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3550454.3555447","volume":"41","author":"Z Chen","year":"2022","unstructured":"Chen, Z., Wang, G., & Liu, Z. (2022). Text2light: Zero-shot text-driven hdr panorama generation. ACM Transactions on Graphics (TOG), 41(6), 1\u201316.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"2629_CR44","doi-asserted-by":"crossref","unstructured":"Dastjerdi, M.R.K., Hold-Geoffroy, Y., Eisenmann, J., Khodadadeh, S., & Lalonde, J.-F. (2022). Guided co-modulated gan for 360$$^{\\circ }$$ field of view extrapolation. In: 2022 International Conference on 3D Vision (3DV), pp. 475\u2013485. IEEE","DOI":"10.1109\/3DV57658.2022.00059"},{"key":"2629_CR45","doi-asserted-by":"crossref","unstructured":"Akimoto, N., Matsuo, Y., & Aoki, Y.(2022). Diverse plausible 360-degree image outpainting for efficient 3dcg background creation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11441\u201311450","DOI":"10.1109\/CVPR52688.2022.01115"},{"key":"2629_CR46","doi-asserted-by":"crossref","unstructured":"Ai, H., Cao, Z., Lu, H., Chen, C., Ma, J., Zhou, P., Kim, T.-K., Hui, P., & Wang, L.(2024). Dream360: Diverse and immersive outdoor virtual scene creation via transformer-based 360$$^{\\circ }$$ image outpainting. IEEE Transactions on Visualization and Computer Graphics (TVCG)","DOI":"10.1109\/TVCG.2024.3372085"},{"key":"2629_CR47","unstructured":"Li, W., Mi, Y., Cai, F., Yang, Z., Zuo, W., Wang, X., & Fan, X.(2024). Scenedreamer360: Text-driven 3d-consistent scene generation with panoramic gaussian splatting. arXiv:2408.13711"},{"key":"2629_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Song, J., Huang, X., Chen, Y., & Liu, M.-Y.(2023). Diffcollage: Parallel generation of large content with diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10188\u201310198. IEEE","DOI":"10.1109\/CVPR52729.2023.00982"},{"key":"2629_CR49","unstructured":"Li, J., & Bansal, M.(2023). Panogen: Text-conditioned panoramic environment generation for vision-and-language navigation. In: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2629_CR50","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Z., Ling, J., Xie, R., & Song, L.(2023). 360-degree panorama generation from few unregistered nfov images. In: Proceedings of the 31th ACM International Conference on Multimedia (ACM MM)","DOI":"10.1145\/3581783.3612508"},{"key":"2629_CR51","doi-asserted-by":"crossref","unstructured":"Wang, H., Xiang, X., Fan, Y., & Xue, J.-H.(2024). Customizing 360-degree panoramas through text-to-image diffusion models. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4933\u20134943","DOI":"10.1109\/WACV57701.2024.00486"},{"key":"2629_CR52","doi-asserted-by":"crossref","unstructured":"Wu, T., Zheng, C., & Cham, T.-J.(2024). Panodiffusion: 360-degree panorama outpainting via diffusion. In: Proceedings of the International Conference on Learning Representations (ICLR)","DOI":"10.1109\/CW64301.2024.00073"},{"key":"2629_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, C., Wu, Q., Gambardella, C.C., Huang, X., Phung, D., Ouyang, W., & Cai, J.(2024). Taming stable diffusion for text to 360 panorama image generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6347\u20136357","DOI":"10.1109\/CVPR52733.2024.00607"},{"key":"2629_CR54","doi-asserted-by":"crossref","unstructured":"Yang, S., Tan, J., Zhang, M., Wu, T., Li, Y., Wetzstein, G., Liu, Z., & Lin, D.(2024). Layerpano3d: Layered 3d panorama for hyper-immersive scene generation. arXiv:2408.13252","DOI":"10.1145\/3721238.3730643"},{"key":"2629_CR55","unstructured":"Li, R., Pan, P., Yang, B., Xu, D., Zhou, S., Zhang, X., Li, Z., Kadambi, A., Wang, Z., & Fan, Z.(2024). 4k4dgen: Panoramic 4d generation at 4k resolution. arXiv:2406.13527"},{"key":"2629_CR56","unstructured":"Ye, W., Ji, C., Chen, Z., Gao, J., Huang, X., Zhang, S.-H., Ouyang, W., He, T., Zhao, C., & Zhang, G.(2024). Diffpano: Scalable and consistent text to panorama generation with spherical epipolar-aware diffusion. In: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2629_CR57","unstructured":"Van Den\u00a0Oord, A., & Vinyals, O., et al.(2017). Neural discrete representation learning. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) 30"},{"key":"2629_CR58","unstructured":"Jia, X., Zhao, Y., Chan, K.C., Li, Y., Zhang, H., Gong, B., Hou, T., Wang, H., & Su, Y.-C.(2023). Taming encoder for zero fine-tuning image customization with text-to-image diffusion models. arXiv:2304.02642"},{"key":"2629_CR59","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., & Clark, J. (2021). Learning transferable visual models from natural language supervision. In: Proceedings of the International Conference on Learning Representations (ICLR), pp. 8748\u20138763. PMLR"},{"key":"2629_CR60","first-page":"26565","volume":"35","author":"T Karras","year":"2022","unstructured":"Karras, T., Aittala, M., Aila, T., & Laine, S. (2022). Elucidating the design space of diffusion-based generative models. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 35, 26565\u201326577.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2629_CR61","unstructured":"Ku, M., Wei, C., Ren, W., Yang, H., & Chen, W.(2024). Anyv2v: A tuning-free framework for any video-to-video editing tasks. arXiv:2403.14468"},{"key":"2629_CR62","doi-asserted-by":"crossref","unstructured":"Bai, J., He, T., Wang, Y., Guo, J., Hu, H., Liu, Z., & Bian, J.(2024). Uniedit: A unified tuning-free framework for video motion and appearance editing. arXiv:2402.13185","DOI":"10.1145\/3746027.3755462"},{"key":"2629_CR63","unstructured":"Hu, T., Zhang, J., Yi, R., Wang, Y., Huang, H., Weng, J., Wang, Y., & Ma, L.(2024). Motionmaster: Training-free camera motion transfer for video generation. arXiv:2404.15789"},{"key":"2629_CR64","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\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2629_CR65","unstructured":"Mou, C., Cao, M., Wang, X., Zhang, Z., Shan, Y., & Zhang, J.(2024). Revideo: Remake a video with motion and content control. In: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2629_CR66","doi-asserted-by":"crossref","unstructured":"Karaev, N., Rocco, I., Graham, B., Neverova, N., Vedaldi, A., & Rupprecht, C. (2024). Cotracker: It is better to track together. Proceedings of the European Conference on Computer Vision (ECCV)","DOI":"10.1007\/978-3-031-73033-7_2"},{"key":"2629_CR67","doi-asserted-by":"crossref","unstructured":"Chen, H., Hou, Y., Qu, C., Testini, I., Hong, X., & Jiao, J.(2024). 360+x: A panoptic multi-modal scene understanding dataset. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19373\u201319382","DOI":"10.1109\/CVPR52733.2024.01833"},{"key":"2629_CR68","unstructured":"Unterthiner, T., Van\u00a0Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., & Gelly, S.(2018). Towards accurate generative models of video: A new metric & challenges. arXiv:1812.01717"},{"key":"2629_CR69","unstructured":"Seitzer, M.(2020). pytorch-fid: FID Score for PyTorch"},{"key":"2629_CR70","unstructured":"Loshchilov, I.(2017). Decoupled weight decay regularization. arXiv:1711.05101"},{"key":"2629_CR71","doi-asserted-by":"crossref","unstructured":"Deng, X., Wang, H., Xu, M., Guo, Y., Song, Y., & Yang, L.(2021). Lau-net: Latitude adaptive upscaling network for omnidirectional image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9189\u20139198","DOI":"10.1109\/CVPR46437.2021.00907"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02629-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-025-02629-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02629-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T15:42:34Z","timestamp":1771602154000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-025-02629-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":71,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2629"],"URL":"https:\/\/doi.org\/10.1007\/s11263-025-02629-7","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"20 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"44"}}