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EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
Video EditingContent GenerationArtificial Intelligence
Abstract:

Recent advances in foundation models highlight a clear trend toward unification and scaling, showing emergent capabilities across diverse domains. While image generation and editing have rapidly transitioned from task-specific to unified frameworks, video generation and editing remain fragmented due to architectural limitations and data scarcity. In this work, we introduce EditVerse, a unified framework for image and video generation and editing within a single model. By representing all modalities, i.e., text, image, and video, as a unified token sequence, EditVerse leverages self-attention to achieve robust in-context learning, natural cross-modal knowledge transfer, and flexible handling of inputs and outputs with arbitrary resolutions and durations. To address the lack of video editing training data, we design a scalable data pipeline that curates 232K video editing samples and combines them with large-scale image and video datasets for joint training. Furthermore, we present EditVerseBench, the first benchmark for instruction-based video editing covering diverse tasks and resolutions. Extensive experiments and user studies demonstrate that EditVerse achieves state-of-the-art performance, surpassing existing open-source and commercial models, while exhibiting emergent editing and generation abilities across modalities.

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

EditVerse proposes a unified framework for image and video generation and editing within a single model, representing all modalities as token sequences and leveraging self-attention for in-context learning. The paper resides in the Cross-Modal Unified Architectures leaf, which contains five papers including EditVerse itself. This leaf sits within the broader Unified Multimodal Generation and Editing Frameworks branch, indicating a moderately populated research direction focused on integrating multiple modalities through shared representations. The presence of four sibling papers suggests active but not overcrowded exploration of cross-modal unification strategies.

The taxonomy reveals that EditVerse's leaf is adjacent to CLIP-Based Multimodal Adaptation and Multimodal Synthesis Taxonomies and Surveys within the same parent branch, while neighboring branches address Image-to-Video Generation, Video Editing, and Text-Guided Synthesis. The scope note for Cross-Modal Unified Architectures explicitly excludes models processing modalities separately, positioning EditVerse among frameworks that pursue deep integration rather than modular pipelines. This placement suggests the work engages with a specific architectural philosophy—unified token representations—that distinguishes it from trajectory-controlled or diffusion-based video editing approaches found in adjacent branches.

Among thirty candidates examined, the analysis identifies potential overlap for all three contributions. The unified framework contribution examined ten candidates with one appearing to refute it, suggesting some prior work on cross-modal architectures exists but is not overwhelming. The scalable data pipeline contribution shows similar statistics (ten candidates, one refutable), indicating limited but non-zero precedent for video editing data curation. The EditVerseBench benchmark examined ten candidates with four refutable, pointing to more substantial prior work on video editing evaluation frameworks. These statistics reflect a focused semantic search rather than exhaustive coverage.

Based on the limited search scope of thirty semantically similar papers, EditVerse appears to occupy a moderately explored niche within cross-modal unification. The framework contribution shows relatively sparse prior work, while the benchmark faces more competition from existing evaluation efforts. The analysis does not capture the full landscape of video editing research, particularly work outside the top-K semantic matches or recent preprints, leaving open questions about how EditVerse's specific design choices compare to the broader literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
6
Refutable Paper

Research Landscape Overview

Core task: unified image and video editing and generation. The field has evolved from early conditional generation methods (Conditional GANs[1]) into a rich ecosystem organized around several major branches. Unified Multimodal Generation and Editing Frameworks explore architectures that handle both images and videos within a single model, often leveraging cross-modal representations (NUWA[17], Unified Model[16]). Image-to-Video Generation and Manipulation focuses on animating static content (Pix2Video[15], Make it Move[39]), while Video Editing and Manipulation addresses temporal consistency and structure-preserving transformations (Structure Content Video[10]). Text-Guided Image and Video Synthesis emphasizes language-driven control (Controllable Synthesis[50]), and Domain-Specific Video Generation targets specialized applications such as talking heads (Talking Head Taxonomy[24]) or robotic manipulation (Robotic Manipulation Video[20]). Controllable Video Generation Mechanisms and 3D-Aware and Attribute Manipulation branches investigate fine-grained spatial and temporal control (ManipDreamer3D[11], Custom Attributes 3D[48]), while Media Authentication and Deepfake Detection (Universal Synthetic Detector[4], Deepfakes Criminalisation[36]) and Accessibility and Creative Tool Design (Accessible Content Creation[18]) address societal and usability concerns. Recent work has concentrated on bridging modalities and unifying editing pipelines. Cross-modal unified architectures, such as VACE[3] and Dreamve[5], aim to share representations across image and video domains, reducing redundancy and enabling seamless transitions between tasks. EditVerse[0] sits squarely within this cross-modal cluster, emphasizing a unified framework that handles diverse editing operations across both images and videos. Compared to VACE[3], which may prioritize video-centric temporal modeling, and Dreamve[5], which explores dream-like generative aesthetics, EditVerse[0] appears to balance generality with practical editing workflows. Meanwhile, neighboring efforts like Image Manifold Pathways[2] investigate latent-space navigation for controllable synthesis, highlighting ongoing debates about whether to design task-agnostic architectures or specialize for particular modalities. These contrasting approaches reflect broader questions in the field: how to achieve true cross-modal unification without sacrificing quality, and how to balance flexibility with computational efficiency in large-scale generative models.

Claimed Contributions

EditVerse unified framework for image and video editing and generation

The authors propose EditVerse, a unified framework that handles both image and video generation and editing tasks in a single model. By representing all modalities (text, image, video) as a unified token sequence and using full self-attention, the framework enables in-context learning, cross-modal knowledge transfer, and flexible handling of arbitrary resolutions and durations.

10 retrieved papers
Can Refute
Scalable data pipeline for video editing

The authors develop a scalable data pipeline to address the scarcity of video editing training data. This pipeline curates 232K video editing samples using task-specific models and filtering, then combines them with large-scale image and video datasets for joint training.

10 retrieved papers
Can Refute
EditVerseBench benchmark for instruction-based video editing

The authors introduce EditVerseBench, the first benchmark designed for instruction-based video editing. It contains 100 videos (50 horizontal and 50 vertical) with two editing prompts each, spanning 20 distinct video editing categories to enable comprehensive evaluation.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

EditVerse unified framework for image and video editing and generation

The authors propose EditVerse, a unified framework that handles both image and video generation and editing tasks in a single model. By representing all modalities (text, image, video) as a unified token sequence and using full self-attention, the framework enables in-context learning, cross-modal knowledge transfer, and flexible handling of arbitrary resolutions and durations.

Contribution

Scalable data pipeline for video editing

The authors develop a scalable data pipeline to address the scarcity of video editing training data. This pipeline curates 232K video editing samples using task-specific models and filtering, then combines them with large-scale image and video datasets for joint training.

Contribution

EditVerseBench benchmark for instruction-based video editing

The authors introduce EditVerseBench, the first benchmark designed for instruction-based video editing. It contains 100 videos (50 horizontal and 50 vertical) with two editing prompts each, spanning 20 distinct video editing categories to enable comprehensive evaluation.

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