Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion
A new framework called Steady-Forcing addresses the trade-off between spatial stability and motion continuity in long-horizon fixed-camera nature video generation. It uses components like visual anchors (V-Sink), motion memory (EMA-Sink), and temporal encoding to improve background consistency and fluid dynamics over multi-minute autoregressive rollouts.
[2606.14732] Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion
[Submitted on 2 Jun 2026]
Title:Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion
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Abstract:Autoregressive video diffusion models enable streaming generation but often degrade over long rollouts: static scene layouts drift, while mechanisms that improve spatial stability tend to suppress motion, causing natural flows such as water, fire, or smoke to stagnate. We study this stability-motion trade-off in fixed-camera long-horizon nature video generation, where the two failure modes can be more clearly separated than in moving-camera settings. We propose Steady-Forcing, a memory and training framework combining a persistent visual anchor (V-Sink), an exponential moving-average motion memory (EMA-Sink), block-relative temporal encoding, periodic cache purification, and distillation from a Wan2.1-14B teacher with motion-rewarded priors under task-focused configurations. Together, these components are designed to preserve background identity while sustaining visually plausible fluid dynamics over multi-minute autoregressive rollouts. Evaluations across seven baselines show that Steady-Forcing improves long horizon background consistency and imaging quality, while a blind user study indicates stronger perceived stability and motion continuity. The benchmark evaluation further suggest that generic VBench aggregate scores under-penalize fixed-camera artifacts as well as rewarding drift-induced optical flow as Dynamic Degree while not directly penalizing texture hardening or flow stagnation - motivating future task-specific benchmarks for static-camera nature-flow evaluation. Project page: this https URL
Comments: Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
Cite as: arXiv:2606.14732 [cs.CV]
(or arXiv:2606.14732v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.14732
arXiv-issued DOI via DataCite
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From: Matiur Rahman Minar [view email] [v1] Tue, 2 Jun 2026 07:11:50 UTC (23,856 KB)
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