VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling

arXiv:2610.12104 · cs.CV, cs.AI, cs.MM · Submitted 2026-10-08 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling".

Jane: The gist: VINCIE-NExT introduces a unified framework that transfers editing capability from images to videos through in-context visual demonstrations, alleviating the need for large-scale paired video editing data.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: Let's talk about who wrote this, starting with the title, "VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling." The name itself suggests they are unlocking a capability that was previously locked away by focusing on images.

Jane: And the authors include Leigang Qu and Feng Cheng, who are from National University of Singapore, along with Ziyan Yang, Bangbang Yang, Zhaoyang Huang, Wei Chow, Yicong Li, and Wenjie Wang. It shows a collaboration across different research groups.

Lu: The implication here is that they aren't just trying to patch an existing system; they are proposing a whole new paradigm for how we connect image editing skills to the video domain using in-context learning. It’s about leveraging what models already know about images to guide video generation.

Meng: So, if I understand this right, the paper isn't just saying "we can do better," it's proposing a fundamental shift in how we train models for video tasks by using images as the primary instructional mechanism instead of relying solely on vast amounts of sequential video data.

Lalam: It moves the focus to making image editing pairs—that is, an image and its edited version—the central piece of information, which acts like a spatial blueprint. This sounds like a very efficient way to inject knowledge into the video generation process without needing every single frame from a huge video sequence.

The paper's summary: Tom: So, what’s the actual mechanism they are describing in "VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling"? They are decomposing video editing into that specific chain we talked about earlier, Video to Image to Image to Video.

Jane: The key part is routing the editing intent through the image domain, which is where they have more mature prior knowledge available. They use an image editing pair as this in-context visual demonstration for every output frame during generation.

Lu: And they introduce a novel position encoding called TDFthree dee-RoPE to link those image demonstrations and video frames in a shared spatial coordinate system. This is crucial because it allows them to propagate the pixel-level appearance changes faithfully across all frames, which is what we need for temporal consistency.

Meng: So, the summary boils down to this: they are using image editing pairs as a blueprint, using TDFthree dee-RoPE to map those edits onto video frames consistently throughout the generation process. It's about making sure the edit looks right over time.

Lalam: And they also mention that this entire framework is designed for scalable joint training from heterogeneous data, meaning you can pull in different types of image and video data together under one objective. This allows them to use image editing pairs, video generation data, and other things all at once during training.

The paper's improvements: Tom: Now let's look at what they actually claim as the main advantages of this VINCIE-NExT framework. They point out three key benefits they believe are significant.

Jane: First, sub-task decomposition lets them train on different kinds of data effectively. The I to I stage uses abundant image editing pairs, and the I to V stage uses image-conditioned video generation data. That’s a lot of leverage for training efficiency there.

Lu: Second, they argue that the in-context visual demonstration is more powerful than just using text prompts because it provides a pixel-grounded spatial prior that guides every denoising step during generation, which leads to stronger editing fidelity.

Meng: That ties directly into what we discussed before about the blueprint. It’s not just a suggestion; it’s a spatial constraint that helps guide the AI's decisions at each point in time. That grounding is what makes it more reliable than just asking for something in text.

Lalam: And third, they introduce Chain-of-Editing, or CoE, which provides principled test-time scaling. This means you can improve editing quality by running the sub-task chain as progressive diffusion stages without needing to retrain the model at all.

Conclusion: Tom: So to wrap up with VINCIE-NExT, they are essentially proposing a video editing system that uses image editing skills to edit videos, which cuts down on the need for large-scale paired video data. They achieve this by routing intent through the image domain using their structured chain of sub-tasks.

Jane: The core finding is that by using TDFthree dee-RoPE for pixel-faithful propagation and CoE for test-time scaling, they can achieve state-of-the-art performance on benchmarks like OpenVE-Bench, showing a relative gain over methods like OpenVE-Edit.

Lu: The implication is that grounding video editing in mature image priors opens up a more practical path for complex video creation research because you don't have to gather massive amounts of video data anymore.

Meng: From an engineering standpoint, it means less expensive per-video fine-tuning because the framework is designed for instruction-driven appearance editing of shorter clips, focusing on style transfer or background replacement rather than complex structural edits across frames.

Lalam: And they acknowledge a limitation: the output quality is currently bottlenecked by that intermediate image to image step, meaning any errors made in that keyframe propagate to all the subsequent video frames. Plus, Chain-of-Editing requires two sequential diffusion stages, which adds some compute cost during test time.

Tom: So we’ve seen how VINCIE-NExT uses a structured chain and TDFthree dee-RoPE to ground editing in image priors, giving us scalable training from mixed data and principled scaling for test time. It's a significant step in making video editing more accessible.

Jane: It’s about proving that we can transfer powerful image editing capabilities to the video domain effectively, even when we don't have the perfect massive paired dataset for every task.

Lu: The research suggests that scaling up image-level data will compound this improvement, and it sets a new direction for how we combine multimodal learning objectives.

Meng: For practical deployment, it’s a tool that works well for short clips where you want style or background changes, not necessarily for complex structural edits like adding new objects mid-scene.

Lalam: So the main point is reducing the dependency on huge video datasets by using this VINCIE-NExT framework to route editing intent through images via a V to I to I to V chain.

Leigang Qu, Feng Cheng, Ziyan Yang, Bangbang Yang, Zhaoyang Huang

National University of Singapore · ByteDance Seed Research Institute

cs.CV, cs.AI, cs.MM

Submitted: 2026-10-08

Updated: 2026-10-08

Comments: Accepted to NeurIPS'26. Project page: https://vincie-next.github.io/

Project page: https://vincie-next.github.io/S

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 90/100

The gist: The gist: VINCIE-NExT introduces a unified framework that transfers editing capability from images to videos through in-context visual demonstrations, alleviating the need for large-scale paired

Key concepts

Decomposition of Video Editing
This concept structures the complex task of video editing into a sequence of simpler, composable sub-tasks: first editing an image, then composing it back into a video. This factorization allows the model to leverage existing, well-understood image editing techniques before tackling the full video generation.
TDF3D-RoPE
This is a novel position encoding method that links the spatial locations of patches in an image demonstration with corresponding frames in a video. It ensures that when an appearance change is made to one part of the input image, the model knows exactly which corresponding pixels to modify across all output video frames.
Chain-of-Editing (CoE)
CoE provides a way to scale editing quality at test time without retraining. It executes the sub-task chain progressively; for instance, it first edits an image based on context, and then uses that edited keyframe to guide the next step of video generation. This allows users to improve results by adding more context shots during inference.
Joint In-context Image–Video Modeling
This involves training a single model parameter set to handle multiple editing tasks simultaneously, drawing knowledge from both image and video data. By sharing parameters across the V2I and I2I sub-tasks, the framework enables cross-task learning where image editing priors help improve video generation quality.

Terminology

Summary

The gist: VINCIE-NExT introduces a unified framework that transfers editing capability from images to videos through in-context visual demonstrations, alleviating the need for large-scale paired video editing data.

How it works

VINCIE-NExT decomposes video editing into a structured chain of composable sub-tasks, i.e., Video → Image → Image → Video, routing editing intent through the image domain where mature large-scale priors are readily available The image editing pair (Is, It), synthesized or user-supplied, serves as a spatial blueprint guiding every output frame This framework centers on three components: (i) Decomposition of Video Editing, (ii) Joint In-context Image–Video Modeling, and (iii) Chain-of-Editing.

The decomposition factorizes the joint distribution over the full generation chain as p(Is, It, Vt Vs, c) = p(Is Vs, c)z > p(It Is, Vs, c)z > p(Vt It, Is, Vs, c)z. I→V recomposes the edited image into a temporally consistent target video, conditioned on both the edited frame and the original video structure. TDF3D-RoPE assigns identical relative positions to spatially corresponding patches across image demonstrations and video frames.

Chain-of-Editing

Chain-of-Editing (CoE) provides a principled test-time scaling by executing the sub-task chain as progressive diffusion stages. The two stages are Stage 1 — Image editing (V→I→I), which generates the edited keyframe It by assembling an interleaved 3-shot context. Test-time scaling allows for extending the chain at test time without retraining by prepending more conditioning shots to the context.

Training on Multi-Source Data

The framework enables scalable joint training from heterogeneous data by defining a combined training corpus as D = DV→I ∪ DI→I ∪ DI→V ∪ Dfull. This joint training regime allows the model to share a single set of parameters across all sub-tasks, facilitating cross-task knowledge transfer and enabling image editing to generalize video.

The training data sources are I2I (paired image editing data from OmniEdit [66]), V2V (paired video editing data from OpenVE [21]), and V↔I↔I (our core novel data source). This instruction is then applied independently to each sampled frame using Seedream 4.5 [55], forming a Video → Image → Image chain triplet.

Experiments and Results

VINCIE-NExT was evaluated on OpenVE-Bench [21], comprising 431 video clips across eight editing categories. The evaluation uses an MLLM-based evaluator (Gemini 2.5 Pro) that scores outputs along three dimensions: Instruction Compliance, Consistency & Detail Fidelity, and Visual Quality & Stability.

Table 1 reports the performance comparison of video editing methods across eight editing categories. VINCIE-NExT achieves an overall score of 3.08, surpassing all open-source methods by a substantial margin, with 24% relative gain over OpenVE-Edit, closing the gap to the proprietary Runway Aleph. Category-level analysis shows that Global Style is the standout category (4.17 vs. Runway Aleph’s 3.72).

A key finding from the ablation study is that TDF3D-RoPE alone yields a larger standalone gain than Chain-of-Editing alone, because establishing where to edit is a prerequisite for executing the edit faithfully. Furthermore, Temporal Position Randomization (TPR) is a prerequisite for CoE to work as intended. The interaction between the two components is multiplicative rather than additive.

Conclusion

VINCIE-NExT substantially reduces the dependence on large-scale paired video data by routing editing intent through the image domain via a structured V→I→I→V chain. TDF3D-RoPE propagates pixel-level appearance changes faithfully across every frame, while Chain-of-Editing provides a principled test-time scaling mechanism that trades compute for fidelity without retraining. Evaluations on OpenVE-Bench confirm state-of-the-art performance among state-of-the current methods.

The research demonstrates that grounding video editing in mature image priors opens a practical and scalable path for future complex video creation research. The framework lowers the barrier to high-quality video editing by substantially reducing the need for large-scale paired video data and expensive per-video fine-tuning.

The paper suggests that the dependence on large-scale paired video editing data is substantially reduced, and it expects further scaling of image-level data to compound this improvement. The framework is designed for instruction-driven appearance editing of short video clips, including style transfer, object attribute modification, and background replacement. The framework assumes a static or slowly varying camera.

The paper notes that output quality is bottlenecked by the intermediate I→I step because errors in the edited keyframe propagate to every output frame. It also suggests that Chain-of-Editing requires two sequential diffusion stages; although its latency is lower than ICVE and comparable to Lucy-Edit, longer test-time chains add cost. The framework is not designed for structural edits that require inserting or removing objects across frames.

The research encourages the community to pair advances in generative editing with robust provenance and detection tools when deploying such systems in consumer-facing products. The paper emphasizes that user-supplied image demonstrations extend the framework beyond text instructions. The framework is not designed for structural edits that require inserting or removing objects across frames.

The research is supported by the National Research Foundation, Singapore under its National Large Language Models Funding Initiative (AISG Award No: AISG-NMLP-2024-002. The paper is not designed for structural edits that require inserting or removing objects across frames. The framework assumes a static or slowly varying camera.

Improvements for AI systems

  1. Bold header: Unified Framework for Video Editing via In-Context Modeling

This framework transfers editing capability from images to videos through in-context visual demonstrations, enabling scalable joint training from heterogeneous data under a unified diffusion objective. The system can perform temporally consistent stylization, dynamic background replacement, identity and attribute editing, scene transformation, and object removal while preserving motion and visual coherence.

  1. Bold header: Chain-of-Editing for Principled Test-Time Scaling

The Chain-of-Editing (CoE) strategy decomposes video editing into a structured chain of sub-tasks (Video → Image → Image → Video), allowing for principled test-time scaling: by executing the sub-task chain as progressive diffusion stages, editing quality can be improved by investing additional compute without retraining.

  1. Bold header: Pixel-Faithful Appearance Propagation via TDF3D-RoPE

The introduction of Time Dual-Frequency 3D Rotary Position Encoding (TDF3D-RoPE) links image demonstrations and video frames in a shared spatial coordinate system, enabling the model to precisely localize appearance changes from the image pair and propagate them faithfully to each output frame.

  1. Bold header: Scalable Training from Heterogeneous Corpora

The sub-task decomposition allows for scalable joint training from heterogeneous data, drawing on abundant image editing pairs for I→I, image-conditioned video generation data for I→V, and video–keyframe correspondence data for V→I.

  1. Bold header: Instruction-Grounding Data Construction (V↔I↔I)

The system constructs novel training data by using video-based in-context image editing (V↔I↔I) data, where a source video, an unedited representative frame, and its edited counterpart serve as a demonstration, teaching the model to use image editing priors directly in video generation.

  1. Bold header: Test-Time Extensibility via Chain Extension

The CoE inference strategy allows for test-time scaling by enabling the chain to be extended at test time without retraining, e.g., iterating image editing multiple times before video generation, trading additional compute for higher fidelity.

Abstract

Building a capable video editor remains significantly harder than a video generator: editing requires (source, instruction, edited) triplets that are prohibitively expensive to annotate and difficult to synthesize at scale, whereas image editing has already reached maturity with millions of such pairs readily available. In this work, we introduce VINCIE-NExT, a unified framework that transfers editing capability from images to videos through in-context visual demonstrations, alleviating the need for large-scale paired video editing data. VINCIE-NExT decomposes video editing into a structured chain of composable sub-tasks (Video -> Image -> Image -> Video), routing editing intent through the image domain and enabling scalable joint training from heterogeneous image and video corpora under a unified diffusion objective. An image editing pair, synthesized by the model or supplied by the user, is prepended as an in-context visual demonstration that serves as a spatial appearance blueprint for every output frame. To ground appearance edits across the interleaved context, we introduce a novel position encoding that links image demonstrations and video frames in a shared spatial coordinate system, enabling pixel-faithful propagation of appearance changes to every output frame. Chain-of-Editing further provides principled test-time scaling: by executing the sub-task chain as progressive diffusion stages, editing quality can be improved by investing additional compute without retraining. Comprehensive experiments on OpenVE-Bench demonstrate the state-of-the-art performance across diverse editing categories, with ablations confirming the effectiveness of each component.

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