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Significant advancements have been made in video generative models recently.
The fréchet distance between multivariate normal distributions
Dowson, D. and Landau, B · 1982
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Dalal, N. and Triggs, B · 2005
Earlier work this paper cites.
Particle video: Long-range motion estimation using point trajectories
Sand, P. and Teller, S · 2008
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Learning spatiotemporal features with 3d convolutional networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Earlier work this paper cites.
Generating videos with scene dynamics
Vondrick, C., Pirsiavash, H., and Torralba, A · 2016
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Stochastic variational video prediction
Babaeizadeh, M., Finn, C., Erhan, D., Campbell, R. H., and Levine, S · 2017
Earlier work this paper cites.
Quo vadis, action recognition? a new model and the kinetics dataset
Carreira, J. and Zisserman, A · 2017
Earlier work this paper cites.
Self-supervised visual planning with temporal skip connections
Ebert, F., Finn, C., Lee, A. X., and Levine, S · 2017
Earlier work this paper cites.
Temporal generative adversarial nets with singular value clipping
Saito, M., Matsumoto, E., and Saito, S · 2017
Earlier work this paper cites.
Stochastic video generation with a learned prior
Denton, E. and Fergus, R · 2018
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Stochastic adversarial video prediction
Lee, A. X., Zhang, R., Ebert, F., Abbeel, P., Finn, C., and Levine, S · 2018
Earlier work this paper cites.
Towards accurate generative models of video: A new metric & challenges
Unterthiner, T., Van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., and Gelly, S · 2018
Earlier work this paper cites.
Conditional gan with discriminative filter generation for text-to-video synthesis
Balaji, Y., Min, M. R., Bai, B., Chellappa, R., and Graf, H. P · 2019
Earlier work this paper cites.
Improved conditional vrnns for video prediction
Castrejon, L., Ballas, N., and Courville, A · 2019
Cited alongside, same era.
Quality assessment of in-the-wild videos
Li, D., Jiang, T., and Jiang, M · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Cited alongside, same era.
Few-shot video-to-video synthesis
Wang, T.-C., Liu, M.-Y., Tao, A., Liu, G., Kautz, J., and Catanzaro, B · 2019
Cited alongside, same era.
Stochastic latent residual video prediction
Franceschi, J.-Y., Delasalles, E., Chen, M., Lamprier, S., and Gallinari, P · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2
Skorokhodov, I., Tulyakov, S., and Elhoseiny, M · 2022
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A deep learning based no-reference quality assessment model for ugc videos
Sun, W., Min, X., Lu, W., and Zhai, G · 2022
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Phenaki: Variable length video generation from open domain textual descriptions
Villegas, R., Babaeizadeh, M., Kindermans, P.-J., Moraldo, H., Zhang, H., Saffar, M. T., Castro, S., Kunze, J., and Erhan, D · 2022
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Mcvd-masked conditional video diffusion for prediction, generation, and interpolation
Voleti, V., Jolicoeur-Martineau, A., and Pal, C · 2022
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Magicvideo: Efficient video generation with latent diffusion models
Zhou, D., Wang, W., Yan, H., Lv, W., Zhu, Y., and Feng, J · 2022
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Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan
Saito, M., Saito, S., Koyama, M., and Kobayashi, S · 2020
Cited alongside, same era.
ipoke: Poking a still image for controlled stochastic video synthesis
Blattmann, A., Milbich, T., Dorkenwald, M., and Ommer, B · 2021
Cited alongside, same era.
Stochastic image-to-video synthesis using cinns
Dorkenwald, M., Milbich, T., Blattmann, A., Rombach, R., Derpanis, K. G., and Ommer, B · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Videogpt: Video generation using vq-vae and transformers
Yan, W., Zhang, Y., Abbeel, P., and Srinivas, A · 2021
Cited alongside, same era.
Long video generation with time-agnostic vqgan and time-sensitive transformer
Ge, S., Hayes, T., Yang, H., Yin, X., Pang, G., Jacobs, D., Huang, J.-B., and Parikh, D · 2022
Cited alongside, same era.
Animate anyone: Consistent and controllable image-to-video synthesis for character animation
Hu, L., Gao, X., Zhang, P., Sun, K., Zhang, B., and Bo, L · 2023
Later among the works it cites.
Vbench: Comprehensive benchmark suite for video generative models
Huang, Z., He, Y., Yu, J., Zhang, F., Si, C., Jiang, Y., Zhang, Y., Wu, T., Jin, Q., Chanpaisit, N., et al · 2023
Later among the works it cites.
Disco: Disentangled control for referring human dance generation in real world
Wang, T., Li, L., Lin, K., Lin, C.-C., Yang, Z., Zhang, H., Liu, Z., and Wang, L · 2023
Later among the works it cites.
Magicanimate: Temporally consistent human image animation using diffusion model
Xu, Z., Zhang, J., Liew, J. H., Yan, H., Liu, J.-W., Zhang, C., Feng, J., and Shou, M. Z · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L., Rao, A., and Agrawala, M · 2023
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Pointodyssey: A large-scale synthetic dataset for long-term point tracking
Zheng, Y., Harley, A. W., Shen, B., Wetzstein, G., and Guibas, L. J · 2023
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Video generation models as world simulators
Brooks, T., Peebles, B., Holmes, C., DePue, W., Guo, Y., Jing, L., Schnurr, D., Taylor, J., Luhman, T., Luhman, E., Ng, C., Wang, R., and Ramesh, A · 2024
Closest in time.
Ntire 2024 quality assessment of ai-generated content challenge
Liu, X., Min, X., Zhai, G., Li, C., Kou, T., Sun, W., Wu, H., Gao, Y., Cao, Y., Zhang, Z., et al · 2024
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Kvq: Kaleidoscope video quality assessment for short-form videos
Lu, Y., Li, X., Pei, Y., Yuan, K., Xie, Q., Qu, Y., Sun, M., Zhou, C., and Chen, Z · 2024
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Video diffusion models: A survey
Melnik, A., Ljubljanac, M., Lu, C., Yan, Q., Ren, W., and Ritter, H · 2024
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