Fluid Composer: Fluid Detail Composition and Rendering Using Video Diffusion Models

Abstract We introduce a hybrid pipeline that combines classical fluid simulation with modern generative video models to produce high‐quality, controllable fluid effects without implementationally difficult solvers or costly ray‐tracing. First, a lightweight physics‐based simulator enforces core properties like incompressibility and lets artists specify layout, boundary conditions, and source positions. Second, we render a simple ‘control video’ via real‐time rasterisation (diffuse shading, masks, depth) to capture scene structure and material regions. Third, a text‐guided diffusion transformer (e.g., VACE) treats this control video as a canvas, refining it by adding foam, bubbles, splashes, and realistic colour blending for multiple materials. Our method leverages pretrained video generators' implicit physical priors, while masking and noise‐warping ensure precise, per‐material control and seamless mixtures in latent space. Compared to purely simulation‐based or generative model based text‐only approaches, we avoid implementing specialised multiphase algorithms and expensive rendering passes, yet retain full artistic control over fluid behaviour and appearance. We demonstrate that this training‐free strategy delivers photorealistic fluid videos, supports diverse effects (multiphase flows, transparent media and wet foams), and simplifies the artist's workflow by unifying simulation, shading, and generative rendering in a single, extensible framework.

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Publication Details

Journal
Computer Graphics Forum
Published
2025-12-26
DOI
https://doi.org/10.1111/cgf.70300
Primary Topic
Computer Graphics and Visualization Techniques
Type
article
Field-Weighted Citation Impact
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article

Fluid Composer: Fluid Detail Composition and Rendering Using Video Diffusion Models

Duowen Chen, Z. Lao, Yu Guo, Heather Yu
Computer Graphics Forum
Computer Graphics and Visualization Techniques
article

Fluid Composer: Fluid Detail Composition and Rendering Using Video Diffusion Models

Duowen Chen, Z. Lao, Yu Guo, Heather Yu
article en

Abstract

Abstract We introduce a hybrid pipeline that combines classical fluid simulation with modern generative video models to produce high‐quality, controllable fluid effects without implementationally difficult solvers or costly ray‐tracing. First, a lightweight physics‐based simulator enforces core properties like incompressibility and lets artists specify layout, boundary conditions, and source positions. Second, we render a simple ‘control video’ via real‐time rasterisation (diffuse shading, masks, depth) to capture scene structure and material regions. Third, a text‐guided diffusion transformer (e.g., VACE) treats this control video as a canvas, refining it by adding foam, bubbles, splashes, and realistic colour blending for multiple materials. Our method leverages pretrained video generators' implicit physical priors, while masking and noise‐warping ensure precise, per‐material control and seamless mixtures in latent space. Compared to purely simulation‐based or generative model based text‐only approaches, we avoid implementing specialised multiphase algorithms and expensive rendering passes, yet retain full artistic control over fluid behaviour and appearance. We demonstrate that this training‐free strategy delivers photorealistic fluid videos, supports diverse effects (multiphase flows, transparent media and wet foams), and simplifies the artist's workflow by unifying simulation, shading, and generative rendering in a single, extensible framework.

Computer Graphics Forum
Georgia Institute of Technology (US), Huawei Technologies (United States) (US)
Openalex Percentile: Top 21%
Computer Graphics and Visualization Techniques
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