{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:31:05Z","timestamp":1742988665041,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":70,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819720941"},{"type":"electronic","value":"9789819720958"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-2095-8_10","type":"book-chapter","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T13:01:41Z","timestamp":1711717301000},"page":"177-196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FASSET: Frame Supersampling and\u00a0Extrapolation Using Implicit Neural Representations of\u00a0Rendering Contents"],"prefix":"10.1007","author":[{"given":"Haoyu","family":"Qin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haonan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyang","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanwen","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,30]]},"reference":[{"issue":"3","key":"10_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3390462","volume":"53","author":"S Anwar","year":"2020","unstructured":"Anwar, S., Khan, S., Barnes, N.: A deep journey into super-resolution: a survey. ACM Comput. Surv. 53(3), 1\u201334 (2020)","journal-title":"ACM Comput. Surv."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Bao, W., Lai, W.S., Ma, C., Zhang, X., Gao, Z., Yang, M.H.: Depth-aware video frame interpolation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00382"},{"key":"10_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1467-8659.2012.03002.x","volume":"31","author":"H Bowles","year":"2012","unstructured":"Bowles, H., Mitchell, K., Sumner, R., Moore, J., Gross, M.: Iterative image warping. Comput. Graph. Forum 31, 1 (2012)","journal-title":"Comput. Graph. Forum"},{"issue":"6","key":"10_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3478513.3480553","volume":"40","author":"KM Briedis","year":"2021","unstructured":"Briedis, K.M., Djelouah, A., Meyer, M., McGonigal, I., Gross, M., Schroers, C.: Neural frame interpolation for rendered content. ACM Trans. Graph. 40(6), 1\u20133 (2021)","journal-title":"ACM Trans. Graph."},{"issue":"2","key":"10_CR5","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/MM.2020.2971677","volume":"40","author":"J Burgess","year":"2020","unstructured":"Burgess, J.: RTX on the NVIDIA Turing GPU. IEEE Micro 40(2), 36\u201344 (2020)","journal-title":"IEEE Micro"},{"key":"10_CR6","unstructured":"Chen, H., He, B., Wang, H., Ren, Y., Lim, S.N., Shrivastava, A.: NeRV: neural representations for videos. In: Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems (2021). https:\/\/openreview.net\/forum?id=BbikqBWZTGB"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, S., Wang, X.: Learning continuous image representation with local implicit image function. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8628\u20138638 (2021)","DOI":"10.1109\/CVPR46437.2021.00852"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: VideoINR: learning video implicit neural representation for continuous space-time super-resolution (2022)","DOI":"10.1109\/CVPR52688.2022.00209"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Choi, M., Kim, H., Han, B., Xu, N., Lee, K.M.: Channel attention is all you need for video frame interpolation. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6693"},{"key":"10_CR10","unstructured":"Chowdhury, H., Kawiak, Robert, R., de\u00a0Boer, Ferreira, G., Xavier, L.: Intel XeSS - an AI based super sampling solution for real-time rendering. In: Game Developers Conference (2022)"},{"key":"10_CR11","unstructured":"Deliot, T., Guinier, F., Vanhoey, K.: Real-time style transfer in unity using deep neural networks, November 2020. https:\/\/blogs.unity3d.com\/2020\/11\/25\/real-time-style-transfer-in-unity-using-deep-neural-networks\/"},{"issue":"5","key":"10_CR12","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1109\/TVCG.2019.2898741","volume":"25","author":"G Denes","year":"2019","unstructured":"Denes, G., Maruszczyk, K., Ash, G., Mantiuk, R.K.: Temporal resolution multiplexing: exploiting the limitations of spatio-temporal vision for more efficient VR rendering. IEEE Trans. Visual Comput. Graphics 25(5), 2072\u20132082 (2019)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"issue":"2","key":"10_CR13","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1111\/j.1467-8659.2009.01641.x","volume":"29","author":"P Didyk","year":"2010","unstructured":"Didyk, P., Eisemann, E., Ritschel, T., Myszkowski, K., Seidel, H.P.: Perceptually-motivated real-time temporal upsampling of 3d content for high-refresh-rate displays. Comput. Graph. Forum 29(2), 713\u2013722 (2010)","journal-title":"Comput. Graph. Forum"},{"key":"10_CR14","unstructured":"Didyk, P., Ritschel, T., Eisemann, E., Myszkowski, K., Seidel, H.P.: Adaptive image-space stereo view synthesis. In: Vision, Modeling, and Visualization (2010). The Eurographics Association (2010)"},{"key":"10_CR15","unstructured":"Dupont, E., Goli\u0144ski, A., Alizadeh, M., Teh, Y.W., Doucet, A.: COIN: compression with implicit neural representations (2021). https:\/\/arxiv.org\/abs\/2103.03123"},{"key":"10_CR16","unstructured":"Epic Games: Unreal Engine 4.19: Screen percentage with temporal upsample, March 2018. https:\/\/docs.unrealengine.com\/en-US\/Engine\/Rendering\/ScreenPercentage\/index.html. Accessed Aug 2019"},{"issue":"6","key":"10_CR17","first-page":"1","volume":"40","author":"J Guo","year":"2021","unstructured":"Guo, J., et al.: Extranet: real-time extrapolated rendering for low-latency temporal supersampling. ACM Trans. Graph. 40(6), 1\u20136 (2021)","journal-title":"ACM Trans. Graph."},{"key":"10_CR18","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14672","author":"YX Guo","year":"2022","unstructured":"Guo, Y.X., Chen, G., Dong, Y., Tong, X.: Classifier guided temporal supersampling for real-time rendering. Comput. Graph. Forum (2022). https:\/\/doi.org\/10.1111\/cgf.14672","journal-title":"Comput. Graph. Forum"},{"key":"10_CR19","unstructured":"Harada, T.: Hardware-accelerated ray tracing in amd radeon prorender 2.0 (2020). https:\/\/gpuopen.com\/learn\/radeon-prorender-2-0\/"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Herzog, R., Eisemann, E., Myszkowski, K., Seidel, H.P.: Spatio-temporal upsampling on the GPU. In: Proceedings of the 2010 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, pp. 91\u201398. I3D 2010, Association for Computing Machinery, New York, NY, USA (2010)","DOI":"10.1145\/1730804.1730819"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Jiang, C.M., Sud, A., Makadia, A., Huang, J., Nie\u00dfner, M., Funkhouser, T.: Local implicit grid representations for 3d scenes. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00604"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Jiang, H., Sun, D., Jampani, V., Yang, M.H., Learned-Miller, E., Kautz, J.: Super slomo: high quality estimation of multiple intermediate frames for video interpolation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00938"},{"key":"10_CR24","unstructured":"Kalluri, T., Pathak, D., Chandraker, M., Tran, D.: FLAVR: flow-agnostic video representations for fast frame interpolation (2021)"},{"key":"10_CR25","unstructured":"Karis, B.: High-quality temporal supersampling. In: SIGGRAPH 2014 Advances in Real-Time Rendering in Games Course (2014)"},{"key":"10_CR26","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, T., Chung, T., Pak, D., Ban, Y., Lee, S.: Adacof: adaptive collaboration of flows for video frame interpolation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020","DOI":"10.1109\/CVPR42600.2020.00536"},{"key":"10_CR28","unstructured":"Liu, E.: Dlss 2.0 - image reconstruction for real-time rendering with deep learning. In: Game Developers Conference (2020)"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Liu, H.T.D., Williams, F., Jacobson, A., Fidler, S., Litany, O.: Learning smooth neural functions via Lipchitz regularization. In: ACM SIGGRAPH 2022 Conference Proceedings. SIGGRAPH 2022, Association for Computing Machinery, New York, NY, USA (2022)","DOI":"10.1145\/3528233.3530713"},{"key":"10_CR30","unstructured":"Liying\u00a0Lu, Ruizheng\u00a0Wu, H.L.J.L., Jia, J.: Video frame interpolation with transformer. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2022)"},{"key":"10_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1007\/978-3-319-46466-4_26","volume-title":"Computer Vision \u2013 ECCV 2016","author":"G Long","year":"2016","unstructured":"Long, G., Kneip, L., Alvarez, J.M., Li, H., Zhang, X., Yu, Q.: Learning image matching by simply watching video. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 434\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_26"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Mark, W.R., McMillan, L., Bishop, G.: Post-rendering 3d warping. In: Proceedings of the 1997 Symposium on Interactive 3D Graphics. I3D 1997, Association for Computing Machinery, New York, NY, USA (1997)","DOI":"10.1145\/253284.253292"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Meyer, S., Djelouah, A., McWilliams, B., Sorkine-Hornung, A., Gross, M., Schroers, C.: Phasenet for video frame interpolation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 498\u2013507. IEEE Computer Society, Los Alamitos, CA, USA, June 2018","DOI":"10.1109\/CVPR.2018.00059"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Meyer, S., Wang, O., Zimmer, H., Grosse, M., Sorkine-Hornung, A.: Phase-based frame interpolation for video. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1410\u20131418 (2015)","DOI":"10.1109\/CVPR.2015.7298747"},{"key":"10_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-58452-8_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Mildenhall","year":"2020","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405\u2013421. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_24"},{"issue":"2","key":"10_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3446790","volume":"40","author":"JH Mueller","year":"2021","unstructured":"Mueller, J.H., Neff, T., Voglreiter, P., Steinberger, M., Schmalstieg, D.: Temporally adaptive shading reuse for real-time rendering and virtual reality. ACM Trans. Graph. 40(2), 1\u20134 (2021)","journal-title":"ACM Trans. Graph."},{"issue":"4","key":"10_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530127","volume":"41","author":"T M\u00fcller","year":"2022","unstructured":"M\u00fcller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. 41(4), 1\u20135 (2022)","journal-title":"ACM Trans. Graph."},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Niklaus, S., Liu, F.: Context-aware synthesis for video frame interpolation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00183"},{"key":"10_CR39","doi-asserted-by":"crossref","unstructured":"Niklaus, S., Liu, F.: Softmax splatting for video frame interpolation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020","DOI":"10.1109\/CVPR42600.2020.00548"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Niklaus, S., Mai, L., Liu, F.: Video frame interpolation via adaptive convolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.244"},{"key":"10_CR41","unstructured":"Nvidia: Nvidia DLSS 3.0 (2022). https:\/\/www.nvidia.com\/en-us\/geforce\/technologies\/dlss\/"},{"key":"10_CR42","unstructured":"Oculus: Asynchronous spacewarp (ASW) (2016). https:\/\/developer.oculus.com\/blog\/asynchronous-spacewarp\/\/"},{"key":"10_CR43","doi-asserted-by":"crossref","unstructured":"Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., Geiger, A.: Texture fields: learning texture representations in function space. In: Proceedings IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00463"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: learning continuous signed distance functions for shape representation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00025"},{"key":"10_CR45","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 8024\u20138035 (2019). https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/bdbca288fee7f92f2bfa9f7012727740-Abstract.html"},{"key":"10_CR46","doi-asserted-by":"publisher","unstructured":"Reda, F., Kontkanen, J., Tabellion, E., Sun, D., Pantofaru, C., Curless, B.: FILM: frame interpolation for large motion. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision. ECCV 2022. LNCS, vol. 13667, pp. 250\u2013266. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20071-7_15","DOI":"10.1007\/978-3-031-20071-7_15"},{"issue":"7","key":"10_CR47","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1111\/cgf.13032","volume":"35","author":"B Reinert","year":"2016","unstructured":"Reinert, B., Kopf, J., Ritschel, T., Cuervo, E., Chu, D., Seidel, H.P.: Proxy-guided image-based rendering for mobile devices. Comput. Graph. Forum 35(7), 353\u2013362 (2016)","journal-title":"Comput. Graph. Forum"},{"key":"10_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"10_CR49","unstructured":"Salvi, M.: An excursion in temporal supersampling. Game Developer\u2019s Conference (GDC) 2016 (2016)"},{"key":"10_CR50","unstructured":"Sandy, M., Andersson, J., Barr\u00e9-Brisebois, C.: Directx: Evolving microsoft\u2019s graphics platform. Game Developers Conference 2018 (2018)"},{"issue":"8","key":"10_CR51","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1111\/j.1467-8659.2012.03075.x","volume":"31","author":"D Scherzer","year":"2012","unstructured":"Scherzer, D., et al.: Temporal coherence methods in real-time rendering. Comput. Graph. Forum 31(8), 2378\u20132408 (2012)","journal-title":"Comput. Graph. Forum"},{"key":"10_CR52","doi-asserted-by":"crossref","unstructured":"Schied, C., et al.: Spatiotemporal variance-guided filtering: Real-time reconstruction for path-traced global illumination. In: Proceedings of High Performance Graphics. HPG 2017, Association for Computing Machinery, New York, NY, USA (2017)","DOI":"10.1145\/3105762.3105770"},{"issue":"4","key":"10_CR53","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1109\/TVCG.2017.2657078","volume":"23","author":"A Schollmeyer","year":"2017","unstructured":"Schollmeyer, A., Schneegans, S., Beck, S., Steed, A., Froehlich, B.: Efficient hybrid image warping for high frame-rate stereoscopic rendering. IEEE Trans. Visual Comput. Graphics 23(4), 1332\u20131341 (2017)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"10_CR54","unstructured":"Sitzmann, V., Martel, J.N., Bergman, A.W., Lindell, D.B., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: Proceedings of NeurIPS (2020)"},{"issue":"6","key":"10_CR55","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1109\/TPAMI.2019.2894353","volume":"42","author":"D Sun","year":"2020","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Models matter, so does training: an empirical study of CNNs for optical flow estimation. IEEE Trans. Pattern Anal. Mach. Intell. 42(6), 1408\u20131423 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2019.2894353","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR56","unstructured":"Tancik, M., et al.: Fourier features let networks learn high frequency functions in low dimensional domains. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol.\u00a033, pp. 7537\u20137547. Curran Associates, Inc. (2020)"},{"issue":"10","key":"10_CR57","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","volume":"43","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Chen, J., Hoi, S.C.H.: Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3365\u20133387 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"10_CR58","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.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR59","doi-asserted-by":"crossref","unstructured":"Xiang, X., Tian, Y., Zhang, Y., Fu, Y., Allebach, J.P., Xu, C.: Zooming slow-MO: fast and accurate one-stage space-time video super-resolution. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3370\u20133379, June 2020","DOI":"10.1109\/CVPR42600.2020.00343"},{"key":"10_CR60","doi-asserted-by":"crossref","unstructured":"Xiao, K., Liktor, G., Vaidyanathan, K.: Coarse pixel shading with temporal supersampling. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games. I3D 2018, Association for Computing Machinery, New York, NY, USA (2018)","DOI":"10.1145\/3190834.3190850"},{"issue":"4","key":"10_CR61","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1145\/3386569.3392376","volume":"39","author":"L Xiao","year":"2020","unstructured":"Xiao, L., Nouri, S., Chapman, M., Fix, A., Lanman, D., Kaplanyan, A.: Neural supersampling for real-time rendering. ACM Trans. Graph. 39(4), 142 (2020)","journal-title":"ACM Trans. Graph."},{"key":"10_CR62","doi-asserted-by":"crossref","unstructured":"Xu, G., Xu, J., Li, Z., Wang, L., Sun, X., Cheng, M.M.: Temporal modulation network for controllable space-time video super-resolution. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6384\u20136393 (2021)","DOI":"10.1109\/CVPR46437.2021.00632"},{"key":"10_CR63","unstructured":"Xu, X., Siyao, L., Sun, W., Yin, Q., Yang, M.H.: Quadratic video interpolation. In: NeurIPS (2019)"},{"issue":"1","key":"10_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816814","volume":"35","author":"LQ Yan","year":"2016","unstructured":"Yan, L.Q., Mehta, S.U., Ramamoorthi, R., Durand, F.: Fast 4d sheared filtering for interactive rendering of distribution effects. ACM Trans. Graph. 35(1), 1\u20133 (2016)","journal-title":"ACM Trans. Graph."},{"issue":"2","key":"10_CR65","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1111\/cgf.14018","volume":"39","author":"L Yang","year":"2020","unstructured":"Yang, L., Liu, S., Salvi, M.: A survey of temporal antialiasing techniques. Comput. Graph. Forum 39(2), 607\u2013621 (2020)","journal-title":"Comput. Graph. Forum"},{"issue":"5","key":"10_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1618452.1618481","volume":"28","author":"L Yang","year":"2009","unstructured":"Yang, L., Nehab, D., Sander, P.V., Sitthi-amorn, P., Lawrence, J., Hoppe, H.: Amortized supersampling. ACM Trans. Graph. 28(5), 1\u201312 (2009)","journal-title":"ACM Trans. Graph."},{"key":"10_CR67","doi-asserted-by":"crossref","unstructured":"Yang, L., et al.: Image-based bidirectional scene reprojection. In: Proceedings of the 2011 SIGGRAPH Asia Conference, pp. 1\u201310 (2011)","DOI":"10.1145\/2024156.2024184"},{"key":"10_CR68","doi-asserted-by":"crossref","unstructured":"Yi, Z., Tang, Q., Azizi, S., Jang, D., Xu, Z.: Contextual residual aggregation for ultra high-resolution image inpainting (2020)","DOI":"10.1109\/CVPR42600.2020.00753"},{"issue":"2","key":"10_CR69","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1111\/cgf.142616","volume":"40","author":"Z Zeng","year":"2021","unstructured":"Zeng, Z., Liu, S., Yang, J., Wang, L., Yan, L.Q.: Temporally reliable motion vectors for real-time ray tracing. Comput. Graph. Forum 40(2), 79\u201390 (2021)","journal-title":"Comput. Graph. Forum"},{"key":"10_CR70","doi-asserted-by":"publisher","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 586\u2013595. Computer Vision Foundation\/IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00068","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["Lecture Notes in Computer Science","Computational Visual Media"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2095-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T13:04:36Z","timestamp":1711717476000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2095-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819720941","9789819720958"],"references-count":70,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2095-8_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"30 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Visual Media","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wellington","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvm2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CVM submission system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"212","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}