WildSmoke: Ready-to-Use Dynamic 3D Smoke Assets from a Single Video in
  the Wild
Authors: name: Yuqiu Liu, name: Jialin Song, name: Manolis Savva, name: Wuyang Chen

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).