# Adversarial Video Generation on Complex Datasets

arXiv: https://arxiv.org/abs/1907.06571 · HTML: https://mlsanity.com/paper/1907.06571/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

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

- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [Diffusion Adversarial Post-Training for One-Step Video Generation](https://mlsanity.com/paper/2501.08316/) (2025)
- [Diffusion Models for Video Prediction and Infilling](https://mlsanity.com/paper/2206.07696/) (2022)
- [DriveGAN: Towards a Controllable High-Quality Neural Simulation](https://mlsanity.com/paper/2104.15060/) (2021)
- [Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis](https://mlsanity.com/paper/2402.14797/) (2024)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images](https://mlsanity.com/paper/2207.11148/) (2022)
- [Playable Video Generation](https://mlsanity.com/paper/2101.12195/) (2021)
- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning](https://mlsanity.com/paper/2105.13617/) (2021)
- [CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation](https://mlsanity.com/paper/2107.02408/) (2021)
- [Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction](https://mlsanity.com/paper/2104.06697/) (2021)
- [Strumming to the Beat: Audio-Conditioned Contrastive Video Textures](https://mlsanity.com/paper/2104.02687/) (2021)
- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [Survey of Video Diffusion Models: Foundations, Implementations, and Applications](https://mlsanity.com/paper/2504.16081/) (2025)
- [Simple Video Generation using Neural ODEs](https://mlsanity.com/paper/2109.03292/) (2021)
- [INR-V: A Continuous Representation Space for Video-based Generative Tasks](https://mlsanity.com/paper/2210.16579/) (2022)
- [Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers](https://mlsanity.com/paper/2303.11251/) (2023)
- [Adaptive Video Distillation: Mitigating Oversaturation and Temporal Collapse in Few-Step Generation](https://mlsanity.com/paper/2603.21864/) (2026)
- [MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs](https://mlsanity.com/paper/2303.02906/) (2023)
- [Conditional Loss and Deep Euler Scheme for Time Series Generation](https://mlsanity.com/paper/2102.05313/) (2021)
- [Hierarchical Video Generation for Complex Data](https://mlsanity.com/paper/2106.02719/) (2021)
- [Disentangling Random and Cyclic Effects in Time-Lapse Sequences](https://mlsanity.com/paper/2207.01413/) (2022)
- [A Large-scale Universal Evaluation Benchmark For Face Forgery Detection](https://mlsanity.com/paper/2406.09181/) (2024)
- [Video Generation from Text Employing Latent Path Construction for Temporal Modeling](https://mlsanity.com/paper/2107.13766/) (2021)
- [Pose-guided Generative Adversarial Net for Novel View Action Synthesis](https://mlsanity.com/paper/2110.07993/) (2021)
- [Efficient training for future video generation based on hierarchical disentangled representation of latent variables](https://mlsanity.com/paper/2106.03502/) (2021)
- [A Survey on GAN Acceleration Using Memory Compression Technique](https://mlsanity.com/paper/2108.06626/) (2021)
- [LARNet: Latent Action Representation for Human Action Synthesis](https://mlsanity.com/paper/2110.10899/) (2021)
- [Cascaded Video Generation for Videos In-the-Wild](https://mlsanity.com/paper/2206.00735/) (2022)

## Abstract (arXiv)

Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work. Our proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator. We evaluate on the related tasks of video synthesis and video prediction, and achieve new state-of-the-art Fr'echet Inception Distance for prediction for Kinetics-600, as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset, alongside establishing a strong baseline for synthesis on Kinetics-600.