# Latent Neural Differential Equations for Video Generation

arXiv: https://arxiv.org/abs/2011.03864 · HTML: https://mlsanity.com/paper/2011.03864/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks](https://mlsanity.com/paper/2202.10571/) (2022)
- [Video Probabilistic Diffusion Models in Projected Latent Space](https://mlsanity.com/paper/2302.07685/) (2023)
- [Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition](https://mlsanity.com/paper/2403.14148/) (2024)
- [Dysen-VDM: Empowering Dynamics-aware Text-to-Video Diffusion with LLMs](https://mlsanity.com/paper/2308.13812/) (2023)
- [AID: Adapting Image2Video Diffusion Models for Instruction-guided Video Prediction](https://mlsanity.com/paper/2406.06465/) (2024)
- [3D-Aware Video Generation](https://mlsanity.com/paper/2206.14797/) (2022)
- [MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation](https://mlsanity.com/paper/2502.12632/) (2025)
- [Variational Inference for SDEs Driven by Fractional Noise](https://mlsanity.com/paper/2310.12975/) (2023)
- [Hamiltonian GAN](https://mlsanity.com/paper/2308.11216/) (2023)
- [Discovering Phase Space Structure in Learned Hamiltonian Systems](https://mlsanity.com/paper/2509.23003/) (2025)

## Abstract (arXiv)

Generative Adversarial Networks have recently shown promise for video generation, building off of the success of image generation while also addressing a new challenge: time. Although time was analyzed in some early work, the literature has not adequately grown with temporal modeling developments. We study the effects of Neural Differential Equations to model the temporal dynamics of video generation. The paradigm of Neural Differential Equations presents many theoretical strengths including the first continuous representation of time within video generation. In order to address the effects of Neural Differential Equations, we investigate how changes in temporal models affect generated video quality. Our results give support to the usage of Neural Differential Equations as a simple replacement for older temporal generators. While keeping run times similar and decreasing parameter count, we produce a new state-of-the-art model in 64×64 pixel unconditional video generation, with an Inception Score of 15.20.