Built on

M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM , vol. 24, no. 6, pp. 381–395, 1981
1981
Earlier work this paper cites.
P. C. Ng and S. Henikoff, “Sift: Predicting amino acid changes that affect protein function,” Nucleic acids research , vol. 31, no. 13, pp. 3812–3814, 2003
2003
Earlier work this paper cites.
H. Hirschmuller, “Accurate and efficient stereo processing by semi-global matching and mutual information,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) , vol. 2. IEEE, 2005, pp. 807–814
2005
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4104–4113
2016
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
P. Moulon, P. Monasse, R. Perrot, and R. Marlet, “Openmvg: Open multiple view geometry,” in Reproducible Research in Pattern Recognition: First International Workshop, RRPR 2016, Cancún, Mexico, December 4, 2016, Revised Selected Papers 1 . Springer, 2017, pp. 60–74
2017
Earlier work this paper cites.

Similar

T. Unterthiner, S. van Steenkiste, K. Kurach, R. Marinier, M. Michalski, and S. Gelly, “Fvd: A new metric for video generation,” 2019
2019
Cited alongside, same era.
D. Cernea, “Openmvs: Multi-view stereo reconstruction library,” City , vol. 5, no. 7, 2020
2020
Cited alongside, same era.
Z. Wang, S. Wu, W. Xie, M. Chen, and V. A. Prisacariu, “Nerf–: Neural radiance fields without known camera parameters,” arXiv preprint arXiv:2102.07064 , 2021
Original
2021
Cited alongside, same era.
J. Ho, T. Salimans, A. Gritsenko, W. Chan, M. Norouzi, and D. J. Fleet, “Video diffusion models,” arXiv:2204.03458 , 2022
Original
2022
Cited alongside, same era.
J. Ho, W. Chan, C. Saharia, J. Whang, R. Gao, A. Gritsenko, D. P. Kingma, B. Poole, M. Norouzi, D. J. Fleet et al. , “Imagen video: High definition video generation with diffusion models,” arXiv preprint arXiv:2210.02303 , 2022

Then

A. Blattmann, R. Rombach, H. Ling, T. Dockhorn, S. W. Kim, S. Fidler, and K. Kreis, “Align your latents: High-resolution video synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 22 563–22 575
2023
Later among the works it cites.
Y. Zhou, D. Zhou, Z.-L. Zhu, Y. Wang, Q. Hou, and J. Feng, “Maskdiffusion: Boosting text-to-image consistency with conditional mask,” arXiv preprint arXiv:2309.04399 , 2023
Original
2023
Later among the works it cites.
A. Blattmann, T. Dockhorn, S. Kulal, D. Mendelevitch, M. Kilian, D. Lorenz, Y. Levi, Z. English, V. Voleti, A. Letts et al. , “Stable video diffusion: Scaling latent video diffusion models to large datasets,” arXiv preprint arXiv:2311.15127 , 2023
Original
2023
Later among the works it cites.
RunwayML Team, “RunwayML - Gen2,” https://research.runwayml.com/gen2 , 2023
2023
Later among the works it cites.

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2024

Sora Generates Videos with Stunning Geometrical Consistency

Li, Xuanyi, Zhou, Daquan, Zhang, Chenxu et al.

Understand

The recently developed Sora model [1] has exhibited remarkable capabilities in video generation, sparking intense discussions regarding its ability to simulate real-world phenomena.

Despite its growing popularity, there is a lack of established metrics to evaluate its fidelity to real-world physics quantitatively.
In this paper, we introduce a new benchmark that assesses the quality of the generated videos based on their adherence to real-world physics principles.
We employ a method that transforms the generated videos into 3D models, leveraging the premise that the accuracy of 3D reconstruction is heavily contingent on the video quality.

Built on

M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM , vol. 24, no. 6, pp. 381–395, 1981
1981
Earlier work this paper cites.
P. C. Ng and S. Henikoff, “Sift: Predicting amino acid changes that affect protein function,” Nucleic acids research , vol. 31, no. 13, pp. 3812–3814, 2003
2003
Earlier work this paper cites.
H. Hirschmuller, “Accurate and efficient stereo processing by semi-global matching and mutual information,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) , vol. 2. IEEE, 2005, pp. 807–814
2005
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4104–4113
2016
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
P. Moulon, P. Monasse, R. Perrot, and R. Marlet, “Openmvg: Open multiple view geometry,” in Reproducible Research in Pattern Recognition: First International Workshop, RRPR 2016, Cancún, Mexico, December 4, 2016, Revised Selected Papers 1 . Springer, 2017, pp. 60–74
2017
Earlier work this paper cites.

Similar

T. Unterthiner, S. van Steenkiste, K. Kurach, R. Marinier, M. Michalski, and S. Gelly, “Fvd: A new metric for video generation,” 2019
2019
Cited alongside, same era.
D. Cernea, “Openmvs: Multi-view stereo reconstruction library,” City , vol. 5, no. 7, 2020
2020
Cited alongside, same era.
Z. Wang, S. Wu, W. Xie, M. Chen, and V. A. Prisacariu, “Nerf–: Neural radiance fields without known camera parameters,” arXiv preprint arXiv:2102.07064 , 2021
Original
2021
Cited alongside, same era.
J. Ho, T. Salimans, A. Gritsenko, W. Chan, M. Norouzi, and D. J. Fleet, “Video diffusion models,” arXiv:2204.03458 , 2022
Original
2022
Cited alongside, same era.
J. Ho, W. Chan, C. Saharia, J. Whang, R. Gao, A. Gritsenko, D. P. Kingma, B. Poole, M. Norouzi, D. J. Fleet et al. , “Imagen video: High definition video generation with diffusion models,” arXiv preprint arXiv:2210.02303 , 2022

Then

A. Blattmann, R. Rombach, H. Ling, T. Dockhorn, S. W. Kim, S. Fidler, and K. Kreis, “Align your latents: High-resolution video synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 22 563–22 575
2023
Later among the works it cites.
Y. Zhou, D. Zhou, Z.-L. Zhu, Y. Wang, Q. Hou, and J. Feng, “Maskdiffusion: Boosting text-to-image consistency with conditional mask,” arXiv preprint arXiv:2309.04399 , 2023
Original
2023
Later among the works it cites.
A. Blattmann, T. Dockhorn, S. Kulal, D. Mendelevitch, M. Kilian, D. Lorenz, Y. Levi, Z. English, V. Voleti, A. Letts et al. , “Stable video diffusion: Scaling latent video diffusion models to large datasets,” arXiv preprint arXiv:2311.15127 , 2023
Original
2023
Later among the works it cites.
RunwayML Team, “RunwayML - Gen2,” https://research.runwayml.com/gen2 , 2023
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2022

Cited alongside, same era.

D. Zhou, W. Wang, H. Yan, W. Lv, Y. Zhu, and J. Feng, “Magicvideo: Efficient video generation with latent diffusion models,” arXiv preprint arXiv:2211.11018 , 2022

Original

2022

Cited alongside, same era.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695

2022

Cited alongside, same era.

B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics , vol. 42, no. 4, 2023

2023

Later among the works it cites.

T. Brooks, B. Peebles, C. Homes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. W. Y. Ng, R. Wang, and A. Ramesh, “Video generation models as world simulators,” 2024. [Online]. Available: https://openai.com/research/video-generation-models-as-world-simulators

2024

Closest in time.

Pika Art, “Pika Art – Home,” https://pika.art/home , 2023, accessed: 2024-02-01

2024

Closest in time.