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The advent of next-generation video generation models like \textit{Sora} poses challenges for AI-generated content (AIGC) video quality assessment (VQA).
Methodology for the subjective assessment of the quality of television pictures itu-r recommendation
Int.Telecommun.Union · 2000
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Methodology for the subjective assessment of the quality of television pictures
Series, B · 2012
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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The” something something” video database for learning and evaluating visual common sense
Goyal, R., Ebrahimi Kahou, S., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al · 2017
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The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al · 2017
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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
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Learning to rank for blind image quality assessment, 2019
Gao, F., Tao, D., Gao, X., and Li, X · 2019
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Detecting deep-fake videos from appearance and behavior
Agarwal, S., Farid, H., El-Gaaly, T., and Lim, S.-N · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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spacy: Industrial-strength natural language processing in python
Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Teed, Z. and Deng, J · 2020
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Optical flow based cnn for detection of unlearnt deepfake manipulations
Caldelli, R., Galteri, L., Amerini, I., and Del Bimbo, A · 2021
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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
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Fast-vqa: Efficient end-to-end video quality assessment with fragment sampling
Wu, H., Chen, C., Hou, J., Liao, L., Wang, A., Sun, W., Yan, Q., and Lin, W · 2022
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Cited alongside, same era.
Stable video diffusion: Scaling latent video diffusion models to large datasets
Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., Letts, A., et al · 2023
Cited alongside, same era.
Stablevideo: Text-driven consistency-aware diffusion video editing
Chai, W., Guo, X., Wang, G., and Lu, Y · 2023
Cited alongside, same era.
Measuring the quality of text-to-video model outputs: Metrics and dataset
Seaweed pro
ByteDance · 2024
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Hunyuanvideo: A systematic framework for large video generative models, 2024
Hunyuan, T · 2024
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Open-sora-plan, April 2024
Lab, P.-Y. and etc., T. A · 2024
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Pika 1.5
Labs, P · 2024
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Dream machine
LumaLabs · 2024
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Hailuo ai
MiniMax · 2024
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Chivileva, I., Lynch, P., Ward, T. E., and Smeaton, A. F · 2023
Cited alongside, same era.
Pick-a-pic: An open dataset of user preferences for text-to-image generation
Kirstain, Y., Polyak, A., Singer, U., Matiana, S., Penna, J., and Levy, O · 2023
Cited alongside, same era.
Stablevqa: A deep no-reference quality assessment model for video stability
Kou, T., Liu, X., Sun, W., Jia, J., Min, X., Zhai, G., and Liu, N · 2023
Cited alongside, same era.
Uniformer: Unifying convolution and self-attention for visual recognition
Li, K., Wang, Y., Zhang, J., Gao, P., Song, G., Liu, Y., Li, H., and Qiao, Y · 2023
Cited alongside, same era.
Fetv: A benchmark for fine-grained evaluation of open-domain text-to-video generation
Liu, Y., Li, L., Ren, S., Gao, R., Li, S., Chen, S., Sun, X., and Hou, L · 2023
Cited alongside, same era.
Make-a-video: Text-to-video generation without text-video data
Singer, U., Polyak, A., Hayes, T., Yin, X., An, J., Zhang, S., Hu, Q., Yang, H., Ashual, O., Gafni, O., et al · 2023
Cited alongside, same era.
Imagereward: Learning and evaluating human preferences for text-to-image generation, 2023
Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., and Dong, Y · 2023
Cited alongside, same era.
Deep learning for video-text retrieval: a review
Zhu, C., Jia, Q., Chen, W., Guo, Y., and Liu, Y · 2023
Cited alongside, same era.
Qu, B., Liang, X., Sun, S., and Gao, W · 2024
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Wanxiang video
Tongyi, A · 2024
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Cogvideox: Text-to-video diffusion models with an expert transformer
Yang, Z., Teng, J., Zheng, W., Ding, M., Huang, S., Xu, J., Yang, Y., Hong, W., Zhang, X., Feng, G., et al · 2024
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The dawn of video generation: Preliminary explorations with sora-like models
Zeng, A., Yang, Y., Chen, W., and Liu, W · 2024
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Long-clip: Unlocking the long-text capability of clip
Zhang, B., Zhang, P., Dong, X., Zang, Y., and Wang, J · 2024
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Open-sora: Democratizing efficient video production for all, March 2024
Zheng, Z., Peng, X., Yang, T., Shen, C., Li, S., Liu, H., Zhou, Y., Li, T., and You, Y · 2024
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Ie-bench: Advancing the measurement of text-driven image editing for human perception alignment
Sun, S., Qu, B., Liang, X., Fan, S., and Gao, W · 2025
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