Sketch-based Fluid Video Generation Using Motion-Guided Diffusion Models in Still Landscape Images
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909853938614272 |
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| author | Jin, Hao Xie, Haoran |
| author_facet | Jin, Hao Xie, Haoran |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_15874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sketch-based Fluid Video Generation Using Motion-Guided Diffusion Models in Still Landscape Images Jin, Hao Xie, Haoran Graphics 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. |
| title | Sketch-based Fluid Video Generation Using Motion-Guided Diffusion Models in Still Landscape Images |
| topic | Graphics |
| url | https://arxiv.org/abs/2510.15874 |