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We introduce bounded generation as a generalized task to control video generation to synthesize arbitrary camera and subject motion based only on a given start and end frame.
Debevec, P.E., Taylor, C.J., Malik, J., Levin, G., Borshukov, G., Yu, Y.: Image-based modeling and rendering of architecture with interactive photogrammetry and view-dependent texture mapping. In: 1998 IEEE International Symposium on Circuits and Systems (ISCAS). vol. 5, pp. 514–517. IEEE (1998)
1998
Earlier work this paper cites.
Choi, B.T., Lee, S.H., Ko, S.J.: New frame rate up-conversion using bi-directional motion estimation. IEEE Transactions on Consumer Electronics 46
2000
Earlier work this paper cites.
Ha, T., Lee, S., Kim, J.: Motion compensated frame interpolation by new block-based motion estimation algorithm. IEEE Transactions on Consumer Electronics 50
2004
Earlier work this paper cites.
Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Earlier work this paper cites.
Schönberger, J.L., Zheng, E., Pollefeys, M., Frahm, J.M.: Pixelwise view selection for unstructured multi-view stereo. In: European Conference on Computer Vision (ECCV) (2016)
2016
Earlier work this paper cites.
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 30
2017
Earlier work this paper cites.
Liu, Z., Yeh, R.A., Tang, X., Liu, Y., Agarwala, A.: Video frame synthesis using deep voxel flow. In: Proceedings of the IEEE international conference on computer vision. pp. 4463–4471 (2017)
2017
Earlier work this paper cites.
Niklaus, S., Mai, L., Liu, F.: Video frame interpolation via adaptive convolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 670–679 (2017)
2017
Earlier work this paper cites.
Hedman, P., Philip, J., Price, T., Frahm, J.M., Drettakis, G., Brostow, G.: Deep blending for free-viewpoint image-based rendering. ACM Transactions on Graphics (ToG) 37
2018
Earlier work this paper cites.
Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: Continuous 3d-structure-aware neural scene representations. Advances in Neural Information Processing Systems 32
2019
Earlier work this paper cites.
Unterthiner, T., van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., Gelly, S.: FVD: A new metric for video generation (2019)
2019
Earlier work this paper cites.
Xu, X., Siyao, L., Sun, W., Yin, Q., Yang, M.H.: Quadratic video interpolation. Advances in Neural Information Processing Systems 32
2019
Earlier work this paper cites.
Liu, Y., Xie, L., Siyao, L., Sun, W., Qiao, Y., Dong, C.: Enhanced quadratic video interpolation. In: Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part IV 16. pp. 41–56. Springer (2020)
2020
Earlier work this paper cites.
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: ECCV (2020)
2020
Earlier work this paper cites.
Seitzer, M.: pytorch-fid: FID Score for PyTorch. https://github.com/mseitzer/pytorch-fid (August 2020), version 0.3.0
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
Holynski, A., Curless, B.L., Seitz, S.M., Szeliski, R.: Animating pictures with eulerian motion fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5810–5819 (2021)
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
Sim, H., Oh, J., Kim, M.: Xvfi: extreme video frame interpolation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 14489–14498 (2021)
2021
Earlier work this paper cites.
Trevithick, A., Yang, B.: Grf: Learning a general radiance field for 3d representation and rendering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 15182–15192 (2021)
2021
Earlier work this paper cites.
Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelnerf: Neural radiance fields from one or few images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4578–4587 (2021)
2021
Cited alongside, same era.
Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5470–5479 (2022)
2022
Cited alongside, same era.
Deng, K., Liu, A., Zhu, J.Y., Ramanan, D.: Depth-supervised nerf: Fewer views and faster training for free. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12882–12891 (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42
2023
Later among the works it cites.
2023
Later among the works it cites.
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2022
Cited alongside, same era.
Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van Gool, L.: Repaint: Inpainting using denoising diffusion probabilistic models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11461–11471 (2022)
2022
Cited alongside, same era.
Mahapatra, A., Kulkarni, K.: Controllable animation of fluid elements in still images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3667–3676 (2022)
2022
Cited alongside, same era.
Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG) 41
2022
Cited alongside, same era.
Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S., Geiger, A., Radwan, N.: Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5480–5490 (2022)
2022
Cited alongside, same era.
Reda, F., Kontkanen, J., Tabellion, E., Sun, D., Pantofaru, C., Curless, B.: FILM: Frame interpolation for large motion. In: European Conference on Computer Vision. pp. 250–266. Springer (2022)
2022
Cited alongside, same era.
Bar-Tal, O., Yariv, L., Lipman, Y., Dekel, T.: Multidiffusion: Fusing diffusion paths for controlled image generation (2023)
2023
Cited alongside, same era.
Li, X., Cao, Z., Sun, H., Zhang, J., Xian, K., Lin, G.: 3d cinemagraphy from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4595–4605 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Luo, Z., Chen, D., Zhang, Y., Huang, Y., Wang, L., Shen, Y., Zhao, D., Zhou, J., Tan, T.: Videofusion: Decomposed diffusion models for high-quality video generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10209–10218 (2023)
2023
Later among the works it cites.
Mahapatra, A., Siarohin, A., Lee, H.Y., Tulyakov, S., Zhu, J.Y.: Text-guided synthesis of eulerian cinemagraphs. ACM Transactions on Graphics (TOG) 42
2023
Later among the works it cites.
Mokady, R., Hertz, A., Aberman, K., Pritch, Y., Cohen-Or, D.: Null-text inversion for editing real images using guided diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6038–6047 (2023)
2023
Later among the works it cites.
Tumanyan, N., Geyer, M., Bagon, S., Dekel, T.: Plug-and-play diffusion features for text-driven image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1921–1930 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhou, Z., Tulsiani, S.: Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12588–12597 (2023)
2023
Later among the works it cites.
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2024
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2024
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Qiu, Z., Liu, W., Feng, H., Xue, Y., Feng, Y., Liu, Z., Zhang, D., Weller, A., Schölkopf, B.: Controlling text-to-image diffusion by orthogonal finetuning. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Wang, X., Yuan, H., Zhang, S., Chen, D., Wang, J., Zhang, Y., Shen, Y., Zhao, D., Zhou, J.: Videocomposer: Compositional video synthesis with motion controllability. Advances in Neural Information Processing Systems 36
2024
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