DreamVE: Unified Instruction-based Image and Video Editing

• URL: http://arxiv.org/abs/2508.06080v1
• Date: Fri, 08 Aug 2025 07:20:30 GMT
• Title: DreamVE: Unified Instruction-based Image and Video Editing
• Abstract summary: We introduce DreamVE, a unified model for instruction-based image and video editing.<n>We propose a two-stage training strategy: first image editing, then video editing.<n>We present comprehensive training data pipelines, including collage-based and generative model-based data synthesis.
• Score: 48.59380808274814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instruction-based editing holds vast potential due to its simple and efficient interactive editing format. However, instruction-based editing, particularly for video, has been constrained by limited training data, hindering its practical application. To this end, we introduce DreamVE, a unified model for instruction-based image and video editing. Specifically, We propose a two-stage training strategy: first image editing, then video editing. This offers two main benefits: (1) Image data scales more easily, and models are more efficient to train, providing useful priors for faster and better video editing training. (2) Unifying image and video generation is natural and aligns with current trends. Moreover, we present comprehensive training data synthesis pipelines, including collage-based and generative model-based data synthesis. The collage-based data synthesis combines foreground objects and backgrounds to generate diverse editing data, such as object manipulation, background changes, and text modifications. It can easily generate billions of accurate, consistent, realistic, and diverse editing pairs. We pretrain DreamVE on extensive collage-based data to achieve strong performance in key editing types and enhance generalization and transfer capabilities. However, collage-based data lacks some attribute editing cases, leading to a relative drop in performance. In contrast, the generative model-based pipeline, despite being hard to scale up, offers flexibility in handling attribute editing cases. Therefore, we use generative model-based data to further fine-tune DreamVE. Besides, we design an efficient and powerful editing framework for DreamVE. We build on the SOTA T2V model and use a token concatenation with early drop approach to inject source image guidance, ensuring strong consistency and editability. The codes and models will be released.

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