OpenNovelty.org

Unified In-Context Video Editing

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

Recent advances in text-to-video generation have sparked interest in generative video editing tasks. Previous methods often rely on task-specific architectures (e.g., additional adapter modules) or dedicated customizations (e.g., DDIM inversion), which limit the integration of versatile editing conditions and the unification of various editing tasks. In this paper, we introduce UNified In-Context Video Editing (UNIC), a simple yet effective framework that unifies diverse video editing tasks within a single model in an in-context manner. To achieve this unification, we represent the inputs of various video editing tasks as three types of tokens: the source video tokens, the noisy video latent, and the multi-modal conditioning tokens that vary according to the specific editing task. Based on this formulation, our key insight is to integrate these three types into a single consecutive token sequence and jointly model them using the native attention operations of DiT, thereby eliminating the need for task-specific adapter designs. Nevertheless, direct task unification under this framework is challenging, leading to severe token collisions and task confusion due to the varying video lengths and diverse condition modalities across tasks. To address these, we introduce task-aware RoPE to facilitate consistent temporal positional encoding, and condition bias that enables the model to clearly differentiate different editing tasks. This allows our approach to adaptively perform different video editing tasks by referring the source video and varying condition tokens "in context", and support flexible task composition. To validate our method, we construct a unified video editing benchmark containing six representative video editing tasks. Results demonstrate that our unified approach achieves comparable performance with task specialists and exhibits emergent task composition abilities.

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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

The paper proposes UNIC, a framework unifying diverse video editing tasks by representing inputs as three token types—source video, noisy latent, and multi-modal conditioning—processed jointly through DiT attention without task-specific adapters. Within the taxonomy, it resides in the 'In-Context Learning Based Unification' leaf under 'Unified Multi-Task Video Editing Frameworks', alongside two sibling papers (Unified InContext Video and Editverse). This leaf contains only three papers total, indicating a relatively sparse but emerging research direction focused on token-based unification without modular architectures.

The taxonomy reveals neighboring approaches in 'Modular Multi-Task Architectures' (three papers using specialized components) and 'Generalist Vision-Language Models for Video' (three papers integrating understanding and editing). The scope note for the paper's leaf explicitly excludes methods requiring task-specific adapters, distinguishing it from modular designs. Nearby branches like 'Zero-Shot and Tuning-Free Video Editing' (nine papers across attention control, plug-and-play frameworks, and latent optimization) and 'Controllable and Attribute-Specific Editing' (eleven papers) address complementary challenges—adaptation efficiency and fine-grained control—but lack the unified token-sequence formulation central to this work.

Among twenty-eight candidates examined, the core UNIC framework contribution shows two refutable candidates out of ten examined, suggesting some prior work in unified video editing architectures. The task-aware RoPE contribution examined ten candidates with none clearly refuting it, indicating potential novelty in temporal positional encoding for multi-task contexts. The condition bias mechanism examined eight candidates with no refutations, suggesting this task differentiation approach may be less explored. The limited search scope (top-K semantic matches plus citation expansion) means these statistics reflect a focused sample rather than exhaustive coverage of the field.

Given the sparse taxonomy leaf (three papers) and the limited literature search (twenty-eight candidates), the work appears to occupy an emerging niche in token-based task unification. The framework-level contribution faces some prior overlap, while the technical mechanisms (task-aware RoPE, condition bias) show fewer direct precedents within the examined sample. The analysis captures positioning within a specific research direction but does not claim comprehensive coverage of all related video editing literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
28
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: unifying diverse video editing tasks within a single model. The field has evolved from specialized single-task methods toward frameworks that handle multiple editing operations under one architecture. The taxonomy reveals seven main branches: Unified Multi-Task Video Editing Frameworks consolidate operations like style transfer, object manipulation, and temporal consistency into cohesive systems; Zero-Shot and Tuning-Free Video Editing explores methods such as Fatezero[2] and Video P2P[4] that adapt pretrained models without retraining; Controllable and Attribute-Specific Editing targets fine-grained control over visual properties; Image-to-Video Adaptation and Propagation extends image editing techniques temporally; Specialized Video Editing Tasks addresses domain-specific challenges like inpainting or relighting; Foundation Models for Video Generation and Editing leverages large-scale pretrained architectures such as Lumiere[13] and Movie Gen[18]; and MLLM-Guided and Instruction-Driven Editing uses multimodal language models to interpret natural language instructions for editing operations. Recent work shows a clear tension between flexibility and efficiency: some approaches pursue broad task coverage through large foundation models, while others emphasize lightweight tuning-free adaptation. Within the Unified Multi-Task Frameworks branch, in-context learning has emerged as a promising direction for task unification without exhaustive retraining. Unified InContext Video[0] exemplifies this trend by enabling diverse edits through contextual examples, positioning itself alongside Editverse[3] and UNIC[14], which similarly aim to handle multiple tasks but differ in their reliance on explicit task conditioning versus implicit learning from demonstrations. Compared to Editverse[3], which may employ more structured task encodings, Unified InContext Video[0] emphasizes the flexibility of in-context prompting. Meanwhile, works like Anyv2v[1] and streaming approaches such as Streaming Video Diffusion[5] highlight alternative pathways—cross-frame consistency and real-time processing—that complement the unification agenda by addressing temporal coherence and computational constraints.

Claimed Contributions

Unified In-Context Video Editing (UNIC) framework

The authors propose UNIC, a framework that unifies multiple video editing tasks by representing inputs as three token types (source video, noisy latent, and multi-modal conditions) and modeling them jointly using native DiT attention operations, eliminating the need for task-specific adapter modules.

10 retrieved papers
Can Refute
Task-aware RoPE for consistent temporal positional encoding

The authors introduce task-aware RoPE, which dynamically assigns unique Rotary Positional Embedding indices based on task types to handle varying condition lengths and prevent token collisions across different video editing tasks.

10 retrieved papers
Condition bias for task differentiation

The authors introduce a learnable condition bias for multi-modal condition signals that allows the model to adaptively distinguish between different editing tasks and resolve ambiguity when conditions share the same modality across tasks.

8 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Unified In-Context Video Editing (UNIC) framework

The authors propose UNIC, a framework that unifies multiple video editing tasks by representing inputs as three token types (source video, noisy latent, and multi-modal conditions) and modeling them jointly using native DiT attention operations, eliminating the need for task-specific adapter modules.

Contribution

Task-aware RoPE for consistent temporal positional encoding

The authors introduce task-aware RoPE, which dynamically assigns unique Rotary Positional Embedding indices based on task types to handle varying condition lengths and prevent token collisions across different video editing tasks.

Contribution

Condition bias for task differentiation

The authors introduce a learnable condition bias for multi-modal condition signals that allows the model to adaptively distinguish between different editing tasks and resolve ambiguity when conditions share the same modality across tasks.

Table of Contents