# Sketch-based Fluid Video Generation Using Motion-Guided Diffusion Models in Still Landscape Images

arXiv: https://arxiv.org/abs/2510.15874 · HTML: https://mlsanity.com/paper/2510.15874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 4 of its 4 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 4 of 4)

- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [MotionCtrl: A Unified and Flexible Motion Controller for Video Generation](https://mlsanity.com/paper/2312.03641/) (2023)
- [Animating Pictures with Eulerian Motion Fields](https://mlsanity.com/paper/2011.15128/) (2020)
- [Sketch-Guided Stylized Landscape Cinemagraph Synthesis](https://mlsanity.com/paper/2412.00638/) (2024)

## Abstract (arXiv)

Integrating motion into static images not only enhances visual expressiveness but also creates a sense of immersion and temporal depth, establishing it as a longstanding and impactful theme in artistic expression. Fluid elements such as waterfall, river, and oceans are common features in landscape, but their complex dynamic characteristics pose significant challenges in modeling and controlling their motion within visual computing. Physics-based methods are often used in fluid animation to track particle movement. However, they are easily affected by boundary conditions. Recently, latent diffusion models have been applied to video generation tasks, demonstrating impressive capabilities in producing high-quality and temporally coherent results. However, it is challenging for the existing methods to animate fluid smooth and temporally consistent motion. To solve these issues, this paper introduces a framework for generating landscape videos by animating fluid in still images under the guidance of motion sketches. We propose a finetuned conditional latent diffusion model for generating motion field from user-provided sketches, which are subsequently integrated into a latent video diffusion model via a motion adapter to precisely control the fluid movement.