WildSmoke：从单一野外视频中提取即用型动态3D烟雾素材

WildSmoke: Ready-to-Use Dynamic 3D Smoke Assets from a Single Video in the Wild

September 14, 2025

作者: Yuqiu Liu, Jialin Song, Manolis Savva, Wuyang Chen

cs.AI

摘要

我们提出了一种从单一野外视频中提取并重建动态3D烟雾资产的流程，并进一步整合了交互式模拟以实现烟雾设计与编辑。近年来，3D视觉领域的显著进展极大地提升了流体动力学的重建与渲染能力，支持了真实且时间一致的视图合成。然而，当前的流体重建主要依赖于精心控制的实验室环境，而针对野外拍摄的真实世界视频的研究则相对匮乏。我们识别了重建真实世界视频中烟雾的三个关键挑战，并设计了针对性的技术，包括背景去除的烟雾提取、烟雾粒子与相机姿态的初始化，以及多视角视频的推断。我们的方法不仅在高质量烟雾重建上超越了先前的重建与生成方法（在野外视频上平均PSNR提升+2.22），还通过模拟我们的烟雾资产，实现了流体动力学的多样且逼真的编辑。我们提供了模型、数据及4D烟雾资产，详情请访问[https://autumnyq.github.io/WildSmoke](https://autumnyq.github.io/WildSmoke)。

English

We propose a pipeline to extract and reconstruct dynamic 3D smoke assets from a single in-the-wild video, and further integrate interactive simulation for smoke design and editing. Recent developments in 3D vision have significantly improved reconstructing and rendering fluid dynamics, supporting realistic and temporally consistent view synthesis. However, current fluid reconstructions rely heavily on carefully controlled clean lab environments, whereas real-world videos captured in the wild are largely underexplored. We pinpoint three key challenges of reconstructing smoke in real-world videos and design targeted techniques, including smoke extraction with background removal, initialization of smoke particles and camera poses, and inferring multi-view videos. Our method not only outperforms previous reconstruction and generation methods with high-quality smoke reconstructions (+2.22 average PSNR on wild videos), but also enables diverse and realistic editing of fluid dynamics by simulating our smoke assets. We provide our models, data, and 4D smoke assets at [https://autumnyq.github.io/WildSmoke](https://autumnyq.github.io/WildSmoke).