🔥 ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?

💡 The paper proposes ImageWAM, a new framework for world action models that replaces video generation with pretrained image editing models for robot control. Traditional world action models rely on video generation, which has three major limitations: high computational costs due to dense multi-frame future tokens, wasted capacity on action-irrelevant details, and potential errors in long-horizon future imagination. The authors question the need for video generation in world action models and propose using image editing instead. ImageWAM uses pretrained image editing models to predict robot actions by focusing on action-relevant visual differences and localized visual changes. The model does not decode the target frame at inference time, but rather uses the output of the image editing model as a compact world-action context. The results show that ImageWAM outperforms standard baselines and competitive world action models without requiring additional policy pretraining, and it reduces computational costs to one sixth and latency to one quarter of video-based models. The authors also provide attention analysis that supports the effectiveness of image editing as an alternative to video-based world-action modeling. Overall, the paper demonstrates that image editing can be a more efficient and effective approach to world action modeling, achieving better performance with reduced computational costs.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19531
• PDF: https://arxiv.org/pdf/2606.19531
• Project Page: https://zhangwenyao1.github.io/ImageWAM/

🤖 Models citing this paper:
• https://huggingface.co/yuyangalin/ImageWAM-FLUX.2-4B-RoboTwin
• https://huggingface.co/yuyangalin/ImageWAM-FLUX.2-4B-LIBERO
• https://huggingface.co/yuyangalin/ImageWAM-FLUX.2-9B-LIBERO

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📢 By: https://xn--r1a.website/PaperNexus

#ImageEditingForRobotControl #WorldActionModels #VideoGenerationAlternatives #PretrainedImageModels #RobotControlWithImageEditing