Sign inGet started
Paper · arXiv

SplatFlux: Feed-Forward 4D Gaussian Reconstruction and Generation from Video Latents

This work presents SplatFlux, a feed-forward latent-to-4DGS framework for both video-based 4D reconstruction and conditional 4D generation. Our key observation is that observed videos and conditionally generated content can be represented in the same pretrained video latent space, allowing both tasks to be formulated as latent-to-4DGS decoding. To construct compact and temporally coherent 4D scenes, SplatFlux introduces a static-dynamic decoupled 4D Gaussian representation that explicitly predicts dynamic probabilities for Gaussian primitives. This representation separates static scene structures from moving regions, enabling complete 4D scene construction in a feed-forward manner through...

AuthorsRunmin Zhang, Binglin Ren, Zhu Yu et al.
Primary categorycs.CV
Published20 October 2026
Last updated at source20 October 2026
cs.CV
Embed this entry
<iframe src="https://silytics.si/embed/item/4d696e99-5f14-42e2-be32-f862f77e0c1c" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 21 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.