[2606.19531] ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
[Submitted on 17 Jun 2026]
Title:ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
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Abstract:World Action Models (WAMs) commonly rely on video generation to bridge visual world modeling and robot control. However, video-based WAMs face three coupled limitations: dense multi-frame future tokens make inference costly, full video prediction spends capacity on action-irrelevant temporal and appearance details, and long-horizon future imagination may introduce errors that mislead action prediction. These issues raise a simple question: Does world action model really need video generation? We propose ImageWAM, a simple WAM framework that repurposes pretrained image editing models for robot action prediction. In contrast to video generation, image editing provides a better-matched prior: it only needs to model a target-frame transformation, focuses on action-relevant current-to-target visual differences, and grounds task instructions to localized visual changes through edit pretraining. In practice, ImageWAM does not decode the target frame at inference time; instead, it conditions a flow-matching action expert on the KV caches produced by image-editing denoising, using them as a compact world-action context. ImageWAM outperforms standard VLA baselines and matching competitive WAMs without additional policy pretraining across different simulator and real-world experiments. It also reduces FLOPs to 1/6 and latency to 1/4 of video-based WAMs. Attention analysis further shows that editing caches focus on task-relevant change regions, supporting image editing as an effective alternative to video-based world-action modeling.
Comments: Project Page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
Cite as: arXiv:2606.19531 [cs.CV]
(or arXiv:2606.19531v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.19531
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuyang Zhang [view email] [v1] Wed, 17 Jun 2026 19:25:28 UTC (3,920 KB)
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