ReBind: Multi-Reference Video Editing via Structured Instructions with Explicit Reference Relationships

• URL: http://arxiv.org/abs/2607.14681v1
• Date: Thu, 16 Jul 2026 07:43:27 GMT
• Title: ReBind: Multi-Reference Video Editing via Structured Instructions with Explicit Reference Relationships
• Abstract summary: ReBind is a systematic framework that introduces semantic instructions with embedded reference tokens.<n>We develop ReBind-Instruct, a specialized MLLM that learns to establish explicit bindings between visual attributes and their reference sources.<n>We also develop ReBind-Edit, which enables lightweight adaptation of text-to-video models to coordinate multiple references.
• Score: 53.24877477312131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent diffusion-based video generation models have made significant progress in multi-reference image-conditioned video editing. However, existing methods still struggle to coordinate information from multiple visual sources accurately. We identify a critical deficiency in existing approaches. Existing editing instructions lack explicit reference relationships, and most multimodal large language models (MLLMs) cannot generate them reliably. To address this problem, we propose ReBind, a systematic framework that introduces semantic instructions with embedded reference tokens as the intermediate representation for multi-reference image-conditioned video editing. Our key insight is embedding reference tokens at semantic positions to eliminate ambiguity and establish precise bindings between visual attributes and their sources. We develop ReBind-Instruct, a specialized MLLM that learns to establish explicit bindings between visual attributes and their reference sources through a two-stage progressive scheme for precise reference relationships. We further develop ReBind-Edit, which enables lightweight adaptation of text-to-video models to coordinate multiple references by binding visual attributes to their designated sources. Extensive experiments demonstrate that ReBind substantially outperforms general-purpose MLLMs in instruction quality and achieves state-of-the-art performance among open-source methods on reference image conditioned video editing. Our project webpage: https://rebind-mrv2v.github.io/.

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