# UNIC: Unified In-Context Video Editing

arXiv: https://arxiv.org/abs/2506.04216 · HTML: https://mlsanity.com/paper/2506.04216/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Built on by (most cited first, 30 of 55)

- [UniVideo: Unified Understanding, Generation, and Editing for Videos](https://mlsanity.com/paper/2510.08377/) (2025)
- [EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning](https://mlsanity.com/paper/2509.20360/) (2025)
- [OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Guided Video Editing](https://mlsanity.com/paper/2512.07826/) (2025)
- [MultiShotMaster: A Controllable Multi-Shot Video Generation Framework](https://mlsanity.com/paper/2512.03041/) (2025)
- [InstructX: Towards Unified Visual Editing with MLLM Guidance](https://mlsanity.com/paper/2510.08485/) (2025)
- [JarvisEvo: Towards a Self-Evolving Photo Editing Agent with Synergistic Editor-Evaluator Optimization](https://mlsanity.com/paper/2511.23002/) (2025)
- [ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling](https://mlsanity.com/paper/2603.25746/) (2026)
- [ContextFlow: Training-Free Video Object Editing via Adaptive Context Enrichment](https://mlsanity.com/paper/2509.17818/) (2025)
- [AnyTalker: Scaling Multi-Person Talking Video Generation with Interactivity Refinement](https://mlsanity.com/paper/2511.23475/) (2025)
- [DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation](https://mlsanity.com/paper/2602.12160/) (2026)
- [Omni-Video 2: Scaling MLLM-Conditioned Diffusion for Unified Video Generation and Editing](https://mlsanity.com/paper/2602.08820/) (2026)
- [DreamVVT: Mastering Realistic Video Virtual Try-On in the Wild via a Stage-Wise Diffusion Transformer Framework](https://mlsanity.com/paper/2508.02807/) (2025)
- [VIVA: VLM-Guided Instruction-Based Video Editing with Reward Optimization](https://mlsanity.com/paper/2512.16906/) (2025)
- [LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing](https://mlsanity.com/paper/2606.26740/) (2026)
- [Lance: Unified Multimodal Modeling by Multi-Task Synergy](https://mlsanity.com/paper/2605.18678/) (2026)
- [O-DisCo-Edit: Object Distortion Control for Unified Realistic Video Editing](https://mlsanity.com/paper/2509.01596/) (2025)
- [OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models](https://mlsanity.com/paper/2509.17627/) (2025)
- [EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing](https://mlsanity.com/paper/2512.06065/) (2025)
- [VideoCoF: Unified Video Editing with Temporal Reasoner](https://mlsanity.com/paper/2512.07469/) (2025)
- [SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing](https://mlsanity.com/paper/2603.19228/) (2026)
- [Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning](https://mlsanity.com/paper/2512.09924/) (2025)
- [LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation](https://mlsanity.com/paper/2604.11789/) (2026)
- [JoyAI-Video-Edit: Real-Time Open-Ended Video Editing with Autoregressive Diffusion](https://mlsanity.com/paper/2608.03974/) (2026)
- [VideoCanvas: Unified Video Completion from Arbitrary Spatiotemporal Patches via In-Context Conditioning](https://mlsanity.com/paper/2510.08555/) (2025)
- [LoVoRA: Text-guided and Mask-free Video Object Removal and Addition with Learnable Object-aware Localization](https://mlsanity.com/paper/2512.02933/) (2025)
- [VDOT: Efficient Unified Video Creation via Optimal Transport Distillation](https://mlsanity.com/paper/2512.06802/) (2025)
- [AVControl: Efficient Framework for Training Audio-Visual Controls](https://mlsanity.com/paper/2603.24793/) (2026)
- [IC-Effect: Precise and Efficient Video Effects Editing via In-Context Learning](https://mlsanity.com/paper/2512.15635/) (2025)
- [AutoCut: End-to-end advertisement video editing based on multimodal discretization and controllable generation](https://mlsanity.com/paper/2603.28366/) (2026)
- [LIVEditor-14B: Lightning Unified Video Editing via In-Context Sparse Attention](https://mlsanity.com/paper/2605.04569/) (2026)

## Abstract (arXiv)

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 superior performance on each task and exhibits emergent task composition abilities.