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VOGUE: Unified Understanding, Generation, and Editing for Videos

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
diffusion;multimodal generation
Abstract:

Unified multimodal understanding–generation models have shown promising results in image generation and editing, but remain largely constrained to the image domain. In this work, we present VOGUE, a versatile framework that extends unified modeling to the video domain. VOGUE adopts a dual-stream design, combining a Multimodal Large Language Model (MLLM) for instruction understanding with a Multimodal DiT (MMDiT) for video generation. This design enables accurate interpretation of complex multimodal instructions while preserving visual consistency. Built on this architecture, VOGUE unifies diverse video generation and editing tasks under a single multimodal instruction paradigm and is jointly trained across them. Extensive experiments demonstrate that VOGUE matches or surpasses state-of-the-art task-specific baselines in visual understanding, text/image-to-video generation, in-context video editing and generation. Beyond these core capabilities, the unified design allows VOGUE to generalize to unseen free-form editing tasks, such as green-screening characters or novel task composition (e.g., editing + style transfer) in a single instruction. Notably, VOGUE is the first system to support visual-prompt-based video generation in a unified model, where the MLLM interprets visual prompts and guides the MMDiT in synthesis. To foster future research, our model and code will be released.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

VOGUE proposes a unified framework for video understanding, generation, and editing by coupling a Multimodal Large Language Model with a Multimodal Diffusion Transformer. The paper resides in the MLLM-Guided Video Synthesis and Editing leaf, which contains only three papers including VOGUE itself. This indicates a relatively sparse research direction within the broader taxonomy of fifty papers, suggesting that fully unified MLLM-driven video systems remain an emerging area rather than a saturated one.

The taxonomy reveals that VOGUE's leaf sits within Unified Multimodal Video Frameworks, which also includes All-in-One Video Creation Platforms and Cross-Modal Unified Encoders. Neighboring branches such as Text-to-Video Synthesis and Instruction-Based Video Editing address related but more specialized tasks. The scope notes clarify that VOGUE's leaf excludes pure generation models without MLLM instruction interpretation and editing-only systems, positioning it at the intersection of language-driven control and comprehensive video manipulation rather than in narrower generation or editing categories.

Among thirty candidates examined, the first contribution on unified understanding-generation-editing shows three refutable candidates out of ten examined, indicating some prior work in holistic video frameworks. The dual-stream MLLM-MMDiT architecture has one refutable candidate among ten, suggesting architectural novelty is less contested. The third contribution on generalization to unseen tasks found zero refutable candidates across ten examined, implying this aspect appears more novel within the limited search scope. These statistics reflect a targeted semantic search, not an exhaustive literature review.

Given the limited search scope and the sparse population of the MLLM-Guided Video Synthesis and Editing leaf, VOGUE appears to occupy a relatively underexplored niche. The analysis covers top-thirty semantic matches and does not claim comprehensive coverage of all related work. The contribution-level statistics suggest that while some aspects overlap with existing unified frameworks, the combination of MLLM-guided control and generalization to unseen task compositions may offer incremental advances within this emerging research direction.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
4
Refutable Paper

Research Landscape Overview

Core task: unified video understanding generation and editing. The field has evolved into a rich ecosystem of specialized branches, each addressing distinct aspects of video processing. At the highest level, Unified Multimodal Video Frameworks integrate understanding, generation, and editing capabilities within single architectures, often leveraging multimodal large language models (MLLMs) to guide synthesis and manipulation. Video Generation Methods focus on creating novel video content from various inputs, while Video Editing Techniques emphasize transforming existing footage through temporal and semantic modifications. Video Understanding and Analysis branches develop methods for interpreting video semantics, and several other branches address interaction interfaces, forensics, collaborative systems, content-aware processing, playable environments, entertainment production, and even bimanual manipulation learned from video. Representative works like Univideo[1] and Omni-Video[6] exemplify the push toward holistic multimodal systems, whereas specialized methods such as Structure and content-guided video[4] and SwapVid[7] tackle narrower editing or generation challenges. Within the MLLM-Guided Video Synthesis and Editing cluster, a particularly active line of work explores how language-driven models can unify traditionally separate tasks. VOGUE[0] sits squarely in this branch, emphasizing a cohesive framework that bridges understanding and generation under MLLM guidance. Nearby efforts like Univideo[1] and Omni-Video[6] similarly pursue end-to-end multimodal integration, though they may differ in architectural choices or the granularity of editing controls they expose. In contrast, Uniedit[5] and UniLiP[3] focus more narrowly on editing workflows or specific modality alignments, highlighting trade-offs between generality and task-specific performance. A central open question across these works is how to balance the flexibility of language-based control with the precision required for fine-grained video manipulation, and how to scale such systems to handle long-form or high-resolution content without sacrificing coherence.

Claimed Contributions

VOGUE: A unified framework for video understanding, generation, and editing

The authors introduce VOGUE, a unified multimodal system that combines video understanding, generation, and editing capabilities within a single framework. Unlike prior work limited to images or single video tasks, VOGUE handles diverse video tasks including text-to-video, image-to-video, in-context video generation, and in-context video editing through multimodal instruction following.

10 retrieved papers
Can Refute
Dual-stream architecture combining MLLM and MMDiT

The authors propose a two-stream architecture where an MLLM serves as the understanding branch for interpreting multimodal instructions, while an MMDiT backbone serves as the generation branch. Both streams receive image and video inputs through different encoders, enabling multimodal reasoning while preserving fine-grained visual details crucial for editing and identity preservation.

10 retrieved papers
Can Refute
Generalization to unseen tasks and task compositions

The authors show that VOGUE can generalize beyond its training data in two ways: transferring editing capabilities from image editing to free-form video editing tasks, and composing multiple tasks within a single instruction. This generalization occurs without explicit training on these compositions or free-form video editing data.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

VOGUE: A unified framework for video understanding, generation, and editing

The authors introduce VOGUE, a unified multimodal system that combines video understanding, generation, and editing capabilities within a single framework. Unlike prior work limited to images or single video tasks, VOGUE handles diverse video tasks including text-to-video, image-to-video, in-context video generation, and in-context video editing through multimodal instruction following.

Contribution

Dual-stream architecture combining MLLM and MMDiT

The authors propose a two-stream architecture where an MLLM serves as the understanding branch for interpreting multimodal instructions, while an MMDiT backbone serves as the generation branch. Both streams receive image and video inputs through different encoders, enabling multimodal reasoning while preserving fine-grained visual details crucial for editing and identity preservation.

Contribution

Generalization to unseen tasks and task compositions

The authors show that VOGUE can generalize beyond its training data in two ways: transferring editing capabilities from image editing to free-form video editing tasks, and composing multiple tasks within a single instruction. This generalization occurs without explicit training on these compositions or free-form video editing data.

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