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Fr\'echet Video Distance (FVD), a prominent metric for evaluating video generation models, is known to conflict with human perception occasionally.
The fréchet distance between multivariate normal distributions
DC Dowson and BV Landau · 1982
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Video quality assessment based on structural distortion measurement
Zhou Wang, Ligang Lu, and Alan C Bovik · 2004
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Study of subjective and objective quality assessment of video
Kalpana Seshadrinathan, Rajiv Soundararajan, Alan Conrad Bovik, and Lawrence K Cormack · 2010
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Video (language) modeling: a baseline for generative models of natural videos
MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra · 2014
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Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Temporal segment networks: Towards good practices for deep action recognition
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
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The ”something something” video database for learning and evaluating visual common sense
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, Florian Hoppe, Christian Thurau, Ingo Bax, and Roland Memisevic · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Temporal generative adversarial nets with singular value clipping
Masaki Saito, Eiichi Matsumoto, and Shunta Saito · 2017
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Decomposing motion and content for natural video sequence prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, and Honglak Lee · 2017
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, and Sergey Levine · 2018
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Demystifying mmd gans
Mikolaj Binkowski, Danica J. Sutherland, Michal Arbel, and Arthur Gretton · 2018
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Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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What makes a video a video: Analyzing temporal information in video understanding models and datasets
De-An Huang, Vignesh Ramanathan, Dhruv Mahajan, Lorenzo Torresani, Manohar Paluri, Li Fei-Fei, and Juan Carlos Niebles · 2018
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Video prediction with appearance and motion conditions
Yunseok Jang, Gunhee Kim, and Yale Song · 2018
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Resound: Towards action recognition without representation bias
Yingwei Li, Yi Li, and Nuno Vasconcelos · 2018
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Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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Large-scale study of perceptual video quality
Zeina Sinno and Alan Conrad Bovik · 2018
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Mocogan: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
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Towards accurate generative models of video: A new metric & challenges
Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly · 2018
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Learning to generate time-lapse videos using multi-stage dynamic generative adversarial networks
Wei Xiong, Wenhan Luo, Lin Ma, Wei Liu, and Jiebo Luo · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
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Improved conditional vrnns for video prediction
Lluis Castrejon, Nicolas Ballas, and Aaron Courville · 2019
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Why can’t i dance in the mall? learning to mitigate scene bias in action recognition
Jinwoo Choi, Chen Gao, Joseph CE Messou, and Jia-Bin Huang · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Quality assessment of in-the-wild videos
Dingquan Li, Tingting Jiang, and Ming Jiang · 2019
The role of imagenet classes in fréchet inception distance
Tuomas Kynkäänniemi, Tero Karras, Miika Aittala, Timo Aila, and Jaakko Lehtinen · 2022
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Uniformerv2: Spatiotemporal learning by arming image vits with video uniformer, 2022
Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Limin Wang, and Yu Qiao · 2022
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On aliased resizing and surprising subtleties in gan evaluation
Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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FaceForensics++: Learning to detect manipulated facial images
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 2019
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First order motion model for image animation
Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe · 2019
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2019
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Hype: A benchmark for human eye perceptual evaluation of generative models
Sharon Zhou, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, Li F Fei-Fei, and Michael Bernstein · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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On self-supervised image representations for gan evaluation
Stanislav Morozov, Andrey Voynov, and Artem Babenko · 2020
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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LAION-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
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Make-a-video: Text-to-video generation without text-video data
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al · 2022
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Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2
Ivan Skorokhodov, Sergey Tulyakov, and Mohamed Elhoseiny · 2022
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
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Generating videos with dynamics-aware implicit generative adversarial networks
Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, and Jinwoo Shin · 2022
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Magicvideo: Efficient video generation with latent diffusion models
Daquan Zhou, Weimin Wang, Hanshu Yan, Weiwei Lv, Yizhe Zhu, and Jiashi Feng · 2022
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Latent-shift: Latent diffusion with temporal shift for efficient text-to-video generation
Jie An, Songyang Zhang, Harry Yang, Sonal Gupta, Jia-Bin Huang, Jiebo Luo, and Xi Yin · 2023
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Preserve your own correlation: A noise prior for video diffusion models
Songwei Ge, Seungjun Nah, Guilin Liu, Tyler Poon, Andrew Tao, Bryan Catanzaro, David Jacobs, Jia-Bin Huang, Ming-Yu Liu, and Yogesh Balaji · 2023
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Emu video: Factorizing text-to-video generation by explicit image conditioning, 2023
Rohit Girdhar, Mannat Singh, Andrew Brown, Quentin Duval, Samaneh Azadi, Sai Saketh Rambhatla, Akbar Shah, Xi Yin, Devi Parikh, and Ishan Misra · 2023
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Reuse and diffuse: Iterative denoising for text-to-video generation, 2023
Jiaxi Gu, Shicong Wang, Haoyu Zhao, Tianyi Lu, Xing Zhang, Zuxuan Wu, Songcen Xu, Wei Zhang, Yu-Gang Jiang, and Hang Xu · 2023
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Photorealistic video generation with diffusion models, 2023
Agrim Gupta, Lijun Yu, Kihyuk Sohn, Xiuye Gu, Meera Hahn, Li Fei-Fei, Irfan Essa, Lu Jiang, and José Lezama · 2023
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Vbench: Comprehensive benchmark suite for video generative models, 2023
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin, Yu Qiao, and Ziwei Liu · 2023
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Text2video-zero: Text-to-image diffusion models are zero-shot video generators
Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi · 2023
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Evalcrafter: Benchmarking and evaluating large video generation models, 2023
Yaofang Liu, Xiaodong Cun, Xuebo Liu, Xintao Wang, Yong Zhang, Haoxin Chen, Yang Liu, Tieyong Zeng, Raymond Chan, and Ying Shan · 2023
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Videofusion: Decomposed diffusion models for high-quality video generation
Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liang Wang, Yujun Shen, Deli Zhao, Jingren Zhou, and Tieniu Tan · 2023
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Mostgan-v: Video generation with temporal motion styles
Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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Temporally consistent transformers for video generation, 2023
Wilson Yan, Danijar Hafner, Stephen James, and Pieter Abbeel · 2023
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Show-1: Marrying pixel and latent diffusion models for text-to-video generation, 2023
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