[Submitted on 21 Oct 2026]
Title:Latent Space Jam: Layout Guided Video Synthesis
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Abstract:Adding control to video generation often requires training, which demands additional computational resources, data, or architectural changes. We propose a training-free approach that utilizes pretrained video diffusion models to control object layout and motion using coarse layouts. Our method operates in two passes: first, it steers spatiotemporal placement through prompt-guided cross-attention to produce a coarse visual guide; second, this guide conditions the same model to generate a high-quality, layoutconsistent video. The approach enables coordinated motion with strong temporal consistency, supporting diverse trajectories and object interactions without additional training. Extensive experiments show that our method consistently outperforms prior work in overlap and trajectory accuracy, as well as visual quality across static, dynamic, and multi-object scene settings.
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Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.30390 [cs.CV]
(or arXiv:2610.30390v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.30390
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Asaf Joseph [view email] [v1] Wed, 21 Oct 2026 19:48:57 UTC (21,874 KB)
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