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We present a simple, modular, and generic method that upsamples coarse 3D models by adding geometric and appearance details.
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Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 184–199 (2014)
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 27 (2014)
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in Neural Information Processing Systems (NeurIPS) (2014)
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Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 391–407 (2016)
2016
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Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1646–1654 (2016)
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Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. Advances in Neural Information Processing Systems (NeurIPS) (2016)
2016
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Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems (NeurIPS) (2017)
2017
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Liu, D., Wang, Z., Fan, Y., Liu, X., Wang, Z., Chang, S., Huang, T.: Robust video super-resolution with learned temporal dynamics. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 2507–2515 (2017)
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Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for GANs do actually converge? In: Proceedings of the International Conference on Machine Learning (ICML. PMLR (2018)
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Smith, E., Fujimoto, S., Meger, D.: Multi-view silhouette and depth decomposition for high resolution 3d object representation. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems (NeurIPS), pp. 6479–6489. Curran Associates, Inc. (2018)
2018
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Wang, X., Yu, K., Dong, C., Loy, C.C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 606–615 (2018)
2018
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Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Change Loy, C.: ESRGAN: Enhanced super-resolution generative adversarial networks. In: The European Conference on Computer Vision Workshops (ECCVW) (2018)
2018
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Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
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Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
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Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 286–301 (2018)
2018
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Barron, J.T.: A general and adaptive robust loss function. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
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Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
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Wang, X., Chan, K.C., Yu, K., Dong, C., Loy, C.C.: EDVR: Video restoration with enhanced deformable convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2019)
2019
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Yi, P., Wang, Z., Jiang, K., Shao, Z., Ma, J.: Multi-temporal ultra dense memory network for video super-resolution. IEEE Transactions on Circuits and Systems for Video Technology (2019)
2019
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Bahat, Y., Michaeli, T.: Explorable super resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2716–2725 (2020)
2020
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 33, pp. 6840–6851 (2020)
2020
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Lugmayr, A., Danelljan, M., Van Gool, L., Timofte, R.: SRFlow: Learning the super-resolution space with normalizing flow. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 715–732 (2020)
2020
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Menon, S., Damian, A., Hu, S., Ravi, N., Rudin, C.: PULSE: Self-supervised photo upsampling via latent space exploration of generative models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2437–2445 (2020)
2020
Cited alongside, same era.
Tian, Y., Zhang, Y., Fu, Y., Xu, C.: TDAN: Temporally-deformable alignment network for video super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
Cited alongside, same era.
Zhang, K., Gool, L.V., Timofte, R.: Deep unfolding network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3217–3226 (June 2020)
2020
Cited alongside, same era.
Chan, K.C., Wang, X., Yu, K., Dong, C., Loy, C.C.: BasicVSR: The search for essential components in video super-resolution and beyond. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4947–4956 (2021)
Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 22367–22377 (June 2023)
2023
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Dong, Z., Chen, X., Yang, J., J.Black, M., Hilliges, O., Geiger, A.: AG3D: Learning to generate 3D avatars from 2D image collections. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
2023
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2023
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Haque, A., Tancik, M., Efros, A., Holynski, A., Kanazawa, A.: Instruct-nerf2nerf: Editing 3d scenes with instructions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
2023
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2021
Cited alongside, same era.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM
2021
Cited alongside, same era.
Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: NeuS: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. Advances in Neural Information Processing Systems (NeurIPS) (2021)
2021
Cited alongside, same era.
Wang, X., Xie, L., Dong, C., Shan, Y.: Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data. In: International Conference on Computer Vision Workshops (ICCVW) (2021)
2021
Cited alongside, same era.
Yariv, L., Gu, J., Kasten, Y., Lipman, Y.: Volume rendering of neural implicit surfaces. Advances in Neural Information Processing Systems (NeurIPS) (2021)
2021
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Bautista, M.A., Guo, P., Abnar, S., Talbott, W., Toshev, A., Chen, Z., Dinh, L., Zhai, S., Goh, H., Ulbricht, D., Dehghan, A., Susskind, J.: GAUDI: A neural architect for immersive 3d scene generation (2022)
2022
Cited alongside, same era.
Chan, E.R., Lin, C.Z., Chan, M.A., Nagano, K., Pan, B., De Mello, S., Gallo, O., Guibas, L.J., Tremblay, J., Khamis, S., et al.: Efficient geometry-aware 3D generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16123–16133 (2022)
2022
Cited alongside, same era.
Chan, K.C., Zhou, S., Xu, X., Loy, C.C.: BasicVSR++: Improving video super-resolution with enhanced propagation and alignment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Cited alongside, same era.
Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: TensoRF: Tensorial radiance fields. In: Proceedings of the European Conference on Computer Vision (ECCV) (2022)
2022
Cited alongside, same era.
Huang, X., Li, W., Hu, J., Chen, H., Wang, Y.: RefSR-NeRF: Towards high fidelity and super resolution view synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8244–8253 (June 2023)
2023
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Kang, M., Zhu, J.Y., Zhang, R., Park, J., Shechtman, E., Paris, S., Park, T.: Scaling up GANs for text-to-image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10124–10134 (June 2023)
2023
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Karnewar, A., Vedaldi, A., Novotny, D., Mitra, N.J.: Holodiffusion: Training a 3D diffusion model using 2D images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
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Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3D gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics (ToG)
2023
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Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4681–4690 (2023)
2023
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2023
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2023
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Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
2023
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Shen, Y., Chandaka, B., Lin, Z.H., Zhai, A., Cui, H., Forsyth, D., Wang, S.: Sim-on-wheels: Physical world in the loop simulation for self-driving. IEEE Robotics and Automation Letters (2023)
2023
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Yoon, Y., Yoon, K.J.: Cross-guided optimization of radiance fields with multi-view image super-resolution for high-resolution novel view synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 12428–12438 (June 2023)
2023
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Yu, X., Xu, M., Zhang, Y., Liu, H., Ye, C., Wu, Y., Yan, Z., Liang, T., Chen, G., Cui, S., Han, X.: MVImgNet: A large-scale dataset of multi-view images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
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2023
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Zheng, X.Y., Pan, H., Wang, P.S., Tong, X., Liu, Y., Shum, H.Y.: Locally attentional SDF diffusion for controllable 3D shape generation. ACM Transactions on Graphics (ToG)
2023
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2023
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Sora: Creating video from text
2024
Closest in time.
Lin, C.Y., Fu, Q., Merth, T., Yang, K., Ranjan, A.: FastSR-NeRF: Improving nerf efficiency on consumer devices with a simple super-resolution pipeline. In: WACV (2024)
2024
Closest in time.
Xia, H., Fu, Y., Liu, S., Wang, X.: Rgbd objects in the wild: Scaling real-world 3d object learning from rgb-d videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)
2024
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Xu, Y., Park, T., Zhang, R., Zhou, Y., Shechtman, E., Liu, F., Huang, J.B., Liu, D.: Videogigagan: Towards detail-rich video super-resolution (2024)
2024
Closest in time.