WildSmoke: Ready-to-Use Dynamic 3D Smoke Assets from a Single Video in
  the Wild
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).
68c9ef646e0073c09bd1dda2
Yuqiu Liu
claimed_verified
2025-09-17T12:57:24.504Z
6702258cbc00379a3bad0cfd
/avatars/f07229d3a6fa00a47edcd4f659f9f77b.svg
Yuqiu Liu
user
qiuqiu99
68c9ef646e0073c09bd1dda3
Jialin Song
68c9ef646e0073c09bd1dda4
Manolis Savva
68c9ef646e0073c09bd1dda5
Wuyang Chen
claimed_verified
2025-09-21T13:13:23.718Z
64df97c628d5d234ce0bf83c
https://cdn-avatars.huggingface.co/v1/production/uploads/64df97c628d5d234ce0bf83c/BIEH7Ep4ffytiAcUIPFe-.jpeg
Wuyang Chen
user
wuyangchen
WildSmoke: Ready-to-Use Dynamic 3D Smoke Assets from a Single Video in
  the Wild
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).
A pipeline extracts and reconstructs dynamic 3D smoke from real-world videos, enabling realistic smoke design and editing through interactive simulation.
3D vision fluid dynamics view synthesis background removal smoke particles camera poses multi-view videos PSNR 4D smoke assets