WildSmoke：從單一野外影片中提取即用型動態3D煙霧素材

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

我們提出了一種流程，旨在從單一野外拍攝的視頻中提取並重建動態3D煙霧資產，並進一步整合互動模擬以實現煙霧設計與編輯。近年來，3D視覺技術的發展顯著提升了流體動力學的重建與渲染能力，支持了真實且時間一致的視圖合成。然而，當前的流體重建主要依賴於精心控制的實驗室環境，而對野外捕捉的真實世界視頻的研究則相對不足。我們指出了在重建真實世界視頻中煙霧時面臨的三個關鍵挑戰，並設計了針對性的技術，包括帶有背景去除的煙霧提取、煙霧粒子與相機姿態的初始化，以及多視角視頻的推斷。我們的方法不僅在煙霧重建質量上超越了先前的重建與生成方法（在野外視頻上平均PSNR提升+2.22），還通過模擬我們的煙霧資產，實現了多樣化且真實的流體動力學編輯。我們在[https://autumnyq.github.io/WildSmoke](https://autumnyq.github.io/WildSmoke)提供了我們的模型、數據及4D煙霧資產。

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).