# Video Generative Adversarial Networks: A Review

arXiv: https://arxiv.org/abs/2011.02250 · HTML: https://mlsanity.com/paper/2011.02250/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 30 of 33)

- [Flexible Diffusion Modeling of Long Videos](https://mlsanity.com/paper/2205.11495/) (2022)
- [Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning](https://mlsanity.com/paper/2311.10709/) (2023)
- [VideoPhy: Evaluating Physical Commonsense for Video Generation](https://mlsanity.com/paper/2406.03520/) (2024)
- [VidToMe: Video Token Merging for Zero-Shot Video Editing](https://mlsanity.com/paper/2312.10656/) (2023)
- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [Efficient Video Prediction via Sparsely Conditioned Flow Matching](https://mlsanity.com/paper/2211.14575/) (2022)
- [A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming](https://mlsanity.com/paper/2404.16038/) (2024)
- [Alignment of Diffusion Models: Fundamentals, Challenges, and Future](https://mlsanity.com/paper/2409.07253/) (2024)
- [UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization](https://mlsanity.com/paper/2308.14395/) (2023)
- [A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights](https://mlsanity.com/paper/2407.08428/) (2024)
- [Susceptibility to Influence of Large Language Models](https://mlsanity.com/paper/2303.06074/) (2023)
- [Wireless End-to-End Image Transmission System using Semantic Communications](https://mlsanity.com/paper/2302.13721/) (2023)
- [Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2302.08641/) (2023)
- [MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion](https://mlsanity.com/paper/2410.07659/) (2024)
- [Evolution of Video Generative Foundations](https://mlsanity.com/paper/2604.06339/) (2026)
- [Aerial Diffusion: Text Guided Ground-to-Aerial View Translation from a Single Image using Diffusion Models](https://mlsanity.com/paper/2303.11444/) (2023)
- [Animated Stickers: Bringing Stickers to Life with Video Diffusion](https://mlsanity.com/paper/2402.06088/) (2024)
- [A Survey: Spatiotemporal Consistency in Video Generation](https://mlsanity.com/paper/2502.17863/) (2025)
- [Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey](https://mlsanity.com/paper/2203.03429/) (2022)
- [Vidmento: Creating Video Stories Through Context-Aware Expansion With Generative Video](https://mlsanity.com/paper/2601.22013/) (2026)
- [A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI](https://mlsanity.com/paper/2401.06256/) (2024)
- [Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning](https://mlsanity.com/paper/2406.12736/) (2024)
- [AutoDirector: Online Auto-scheduling Agents for Multi-sensory Composition](https://mlsanity.com/paper/2408.11564/) (2024)
- [Designing ReLU Generative Networks to Enumerate Trees with a Given Tree Edit Distance](https://mlsanity.com/paper/2510.10706/) (2025)
- [Optimal-Transport-Guided Functional Flow Matching for Turbulent Field Generation in Hilbert Space](https://mlsanity.com/paper/2604.05700/) (2026)
- [Deep Fake Detection, Deterrence and Response: Challenges and Opportunities](https://mlsanity.com/paper/2211.14667/) (2022)
- [MeVGAN: GAN-based Plugin Model for Video Generation with Applications in Colonoscopy](https://mlsanity.com/paper/2311.03884/) (2023)
- [The Evolution and Future Perspectives of Artificial Intelligence Generated Content](https://mlsanity.com/paper/2412.01948/) (2024)
- [Video Understanding by Design: How Datasets Shape Video Models](https://mlsanity.com/paper/2509.09151/) (2025)
- [Inference-based GAN Video Generation](https://mlsanity.com/paper/2512.21776/) (2025)

## Abstract (arXiv)

With the increasing interest in the content creation field in multiple sectors such as media, education, and entertainment, there is an increasing trend in the papers that uses AI algorithms to generate content such as images, videos, audio, and text. Generative Adversarial Networks (GANs) in one of the promising models that synthesizes data samples that are similar to real data samples. While the variations of GANs models, in general, have been covered to some extent in several survey papers, to the best of our knowledge, this is among the first survey papers that reviews the state-of-the-art video GANs models. This paper first categorized GANs review papers into general GANs review papers, image GANs review papers, and special field GANs review papers such as anomaly detection, medical imaging, or cybersecurity. The paper then summarizes the main improvements in GANs frameworks that are not initially developed for the video domain but have been adopted in multiple video GANs variations. Then, a comprehensive review of video GANs models is provided under two main divisions according to the presence or non-presence of a condition. The conditional models then further grouped according to the type of condition into audio, text, video, and image. The paper is concluded by highlighting the main challenges and limitations of the current video GANs models. A comprehensive list of datasets, applied loss functions, and evaluation metrics is provided in the supplementary material.