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

Leigang Qu, Feng Cheng, Ziyan Yang, Bangbang Yang, Zhaoyang Huang, Wei Chow, Yicong Li, Wenjie Wang, Tat-Seng Chua, Yan Zeng

arXiv:2610.12104v1cs.CVcs.AIcs.MM

TL;DR

Paired video-editing triplets are costly to obtain, while image editing has large collections of paired examples. VINCIE-NExT routes video editing through an image-demonstration chain, and achieves state-of-the-art performance across diverse OpenVE-Bench categories.

  • Problem

    Video editing requires source, instruction, and edited-video triplets that are prohibitively expensive to annotate and difficult to synthesize at scale.

    Source: §1 · p. 2

  • Method

    VINCIE-NExT uses an image-editing pair as an in-context demonstration in a Video → Image → Image → Video chain, with position encoding linking corresponding image and video patches.

    Source: §1 · p. 2

  • Results

    VINCIE-NExT achieves state-of-the-art performance across diverse editing categories on OpenVE-Bench.

    Source: §1 · p. 3

  • Takeaways & Limitations

    The framework transfers image-editing capability to video through demonstrations, alleviating the need for large-scale paired video-editing data.

    Source: §1 · p. 3

    Output quality is bottlenecked by the intermediate edited-image step: keyframe errors can propagate to every output frame.

    Source: Limitations · p. 30

Abstract

from arXiv · show

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.

1 Introduction

VINCIE-NExT addresses the scarcity of paired video-editing data by routing edits through image demonstrations and a composable video-to-image-to-image-to-video chain. It reports state-of-the-art performance across diverse OpenVE-Bench editing categories.

1 Introduction

  • Video-editing triplets are prohibitively expensive to annotate and difficult to synthesize at scale, unlike the raw video-text pairs used for video generation.The challenge is editing appearance, style, object attributes, or scene content while preserving temporal coherence.Source: §1 · p. 2
  • Image-editing pairs can act as visual demonstrations of desired transformations, drawing on millions of pairs and reducing dependence on large-scale paired video data.The image corpora cover operations including style transfer, object replacement, and background modification.Source: §1 · pp. 2–3
  • VINCIE-NExT decomposes video editing into a Video → Image → Image → Video chain, jointly modeling image and video context.An image-editing pair, synthesized by the model or supplied by the user, is prepended as an appearance blueprint for every output frame.Source: §1 · pp. 2–3
  • TDF3D-RoPE assigns identical relative positions to spatially corresponding patches in image demonstrations and video frames, supporting faithful propagation of appearance changes.Chain-of-Editing first produces an edited image, then uses it as a demonstration for full-video generation.Source: §1 · pp. 2–3
  • The framework trains from heterogeneous data and offers test-time scaling by trading additional compute for higher editing quality without retraining.Its stated advantages include a pixel-grounded spatial prior during denoising and stronger editing fidelity than text prompts alone.Source: §1 · p. 3
  • VINCIE-NExT achieves state-of-the-art performance across diverse editing categories on OpenVE-Bench.Ablations validate the effectiveness of each component.Source: §1 · p. 3

2 Related Work

Related work spans video-generation and editing methods, mature image-editing approaches, and attempts to transfer image-editing capability to video. VINCIE-NExT instead uses image-editing pairs as in-context demonstrations in joint image-video modeling.

Video Generation and Editing

  • Video editing methods include inversion-based attention manipulation [41] [38] [47] [8], temporal-consistency techniques [18] [30] [12], canonical atlases [31] [2] [43], and trained general editors [42] [13] [15] [33] [28].The passage frames persistent data intensity in high-quality video-editor training as motivation for transfer from image editing.Source: §2 · p. 3

Image Editing

  • Image editing includes training-free diffusion manipulation [22], spatial-control adapters, instruction-trained editors [6] [17] [39], and reference-guided approaches.In-context image editing demonstrates operations through example pairs rather than text [25].Source: §2 · p. 3

Editing Ability Transfer from Image to Video

  • Prior image-to-video transfer methods include architecture inflation [5] [4] [3] [81] [20] and framewise editing or propagation [9] [65] [27] [36] [44], which can flicker or miss holistic temporal reasoning.Other training-based methods [33] treat modalities as separate tasks; ICVE [37] and EditVerse [29] use single-pass video-to-video generation without an explicit edited keyframe.Source: §2 · p. 3

3 Methodology

VINCIE-NExT decomposes video editing into a Video → Image → Image → Video chain, using an image editing pair as an in-context blueprint for video generation. Its shared modeling framework combines heterogeneous training data with spatial position encoding and a two-stage inference strategy.

3.1 Decomposition of Video Editing

  • The chain factors editing into keyframe extraction, instruction-guided image editing, and temporally consistent video recomposition, with the temporal mid-frame serving as a safe default keyframe.Training samples diverse temporal positions, making the model robust to the choice of keyframe.Source: §3.1 · p. 4
  • Subtasks draw on separate data sources and are randomly sampled under a unified flow-matching objective, allowing one parameter set to serve the chain.The sources include video–keyframe correspondence, image editing pairs, image-conditioned video generation, and full-chain samples.Source: §3.1 · p. 5

3.2 Joint In-context Image-Video Modeling

  • The full chain is flattened into an interleaved sequence of source video, source keyframe, edited image, and target video, each paired with its own prompt.The image source-to-target pair provides a pixel-grounded spatial prior for every generated video frame; contiguous subchains can also train standalone subtasks.Source: §3.2 · p. 5

3.2.1 Time Dual-Frequency 3D Rotary Position Encoding (TDF3D-RoPE)

  • TDF3D-RoPE combines global shot offsets, which distinguish shots, with shared spatial positions, which link corresponding patches for pixel-faithful edit propagation.Randomized temporal positions for training images discourage the model from relying on temporal identity instead of spatial content.Source: §3.2.1 · pp. 5–6

3.3 Chain-of-Editing

  • Chain-of-Editing first generates an edited keyframe, then generates the target video from the source video and that keyframe using the same backbone.The edited keyframe is passed directly in latent space, avoiding a VAE decode-and-re-encode cycle; the chain can be extended at test time for higher fidelity with additional compute and no retraining.Source: §3.3 · p. 6

3.4 In-context Video-based Image Editing Data Construction

  • Video-based in-context image-editing samples retain the source video alongside the keyframe and edited image, training the same V→I→I substep reused in video generation.The authors identify broader editing knowledge, stronger intermediate edits, and scalability from producing image triplets from arbitrary video frames without costly video pairs.Source: §3.4 · p. 6

4 Experiments

VINCIE-NExT scores 3.08 overall on OpenVE-Bench, surpassing open-source methods by a substantial margin and narrowing the gap to Runway Aleph. Ablations and qualitative results examine its data sources, inference chain, spatial encoding, and intermediate image edits.

4.1 Experimental Setup

  • Training uses 1.20M OmniEdit I2I samples, 2.45M OpenVE V2V* samples reformatted as sub-chains, and 1.63M V↔I↔I samples synthesized from raw videos.The benchmark contains 431 clips across eight editing categories and uses Gemini 2.5 Pro ratings on three dimensions.Source: §4.1 · pp. 6–7

4.2 Performance Comparison

  • 3.08 overall, VINCIE-NExT surpasses all open-source methods by a substantial margin, with a 24% relative gain over OpenVE-Edit and a narrower gap to Runway Aleph.The comparison is on OpenVE-Bench; results are also reported as consistent under Seed1.6-VL.Source: Table 1 · p. 7, §4.2 · p. 7
  • Global Style scores 4.17 versus Runway Aleph’s 3.72, while Local Remove improves from 1.85 to 3.24 over OpenVE-Edit; Camera Edit remains limited by novel-viewpoint synthesis.Local Add and Background Change also have room for improvement because they require synthesizing content absent from the source video.Source: §4.2 · p. 7

4.3 Ablation Study

  • CoE degrades an I2I-only model, but improves the chain-trained V2V*-only model from 2.49 to 2.57; V↔I↔I training already supplies an intermediate edited frame.The full combination of I2I, V2V*, and V↔I↔I with CoE achieves the best overall score in the ablation.Source: §4.3 · pp. 7–8, Table 2 · p. 8
  • Full data + CoE reaches 2.65 (+14.2%), gains +74.1% on Creative Edit without direct V2V supervision, and is human-preferred over V2V*-only + CoE.The human study reports preferences for Prompt Following of 38% versus 24% and Consistency of 28% versus 9%.Source: §4.3 · p. 8
  • TDF3D-RoPE and TPR each improve overall performance alongside CoE; TDF3D-RoPE has the larger standalone gain, while TPR removes a temporal-index bias affecting the intermediate anchor.Each component combination achieves the best result in its respective ablation.Source: §4.3 · p. 9, Figure 4 · p. 9, Figure 5 · p. 9
  • Performance is best with balanced training-data sampling, and replacing self-editing with stronger external image editors improves overall CoE performance.Moderate V↔I↔I upsampling helps chain exposure, while excessive weighting reduces paired-video supervision; I2I performs best at its default ratio. The external editors have complementary category strengths and outperform self-editing overall.Source: §4.3 · pp. 8–9, Figure 3 · p. 8, Figure 6 · p. 9

4.4 Qualitative Results

  • Qualitative examples show that CoE with self-editing eliminates the flickering and ghosting seen in direct V2V inference, while an external editor further improves semantic accuracy without retraining.The examples include scene stylization and localized compositional effects that maintain temporal consistency without task-specific fine-tuning.Source: §4.4 · p. 9

5 Conclusion

VINCIE-NExT routes video-editing intent through a V→I→I→V chain, reducing dependence on paired video data. The paper reports state-of-the-art OpenVE-Bench performance and presents image-grounded editing as a scalable path for video creation research.

5 Conclusion

  • VINCIE-NExT routes editing intent through a V→I→I→V chain, reducing dependence on large-scale paired video data.The framework jointly trains on heterogeneous image and video corpora under a unified flow-matching objective.Source: §5 · p. 9
  • TDF3D-RoPE propagates pixel-level appearance changes across frames, while Chain-of-Editing trades additional compute for fidelity without retraining.Source: §5 · p. 9
  • The paper reports state-of-the-art OpenVE-Bench performance; Figures 7–8 illustrate global and localized edits, plus subject replacement and removal.The conclusion describes grounding video editing in mature image priors as a practical, scalable path for future complex video creation research.Source: §5 · pp. 9–10, Figure 7 · p. 10, Figure 8 · p. 10

A Additional Details of Methodology

The appendix specifies how TDF3D-RoPE assigns positions, how Chain-of-Editing processes its stages, and how video-based image-editing annotations are constructed.

A.1 TDF3D-RoPE: Position Assignment Formulas

  • TDF3D-RoPE combines shot-level offsets with raw intra-video positions, giving corresponding spatial patches zero relative spatial offset.Text tokens receive sequential positions broadcast across three axes; visual tokens use frame timestamps, supporting non-uniform sampling and variable frame rates.Source: §A.1 · p. 19

A.2 Chain-of-Editing: Implementation Details

  • Chain-of-Editing edits a keyframe frame-independently before generating video jointly as a continuous sequence to model cross-frame dependencies.Longer test-time chains can refine intermediate results for higher fidelity at additional compute, without retraining or fine-tuning.Source: §A.2 · p. 19

B In-context Video-based Image Editing Annotation

  • Video-based image-editing annotation balances editing categories, grounds edits in visible content, and constrains instructions for lexical and semantic diversity.The process tracks annotation statistics and uses them to steer away from overused edits.Source: B In-context Video-based Image Editing… · pp. 19–20

B.1 Editing Category Analysis

The annotation pipeline analyzes categories before writing instructions, then creates edited frame pairs with an image editor.

B.1 Editing Category Analysis

  • Category analysis samples five frames and asks GPT-4o to propose applicable categories from a 13-category taxonomy, using live distribution feedback to favor underrepresented categories.Categories are grounded in visible video content, and their labels guide the subsequent instruction-annotation stage.Source: §B.1 · p. 20

B.2 Text Instruction Annotation

  • Text annotation records a video caption, motivation, forward and reversed editing instructions, and affected frame indices to support bidirectional training.Instructions are framed as plausible edits consistent with an assigned category and the observed content.Source: §B.2 · p. 21

B.3 Frame Editing Annotation

  • Seedream 4.5 edits selected keyframes independently while constraining edits to preserve composition, background, and subject identity.The resulting original-frame, edited-frame, and instruction triplets form video-based in-context image-editing training data.Source: §B.3 · p. 22

C.1 Training Data

VINCIE-NExT trains on image pairs, reformatted video-editing pairs, and video-to-image-to-image chain data. The appendix describes the benchmark, implementation settings, an independent evaluation, and ablations of spatial and temporal position design.

C.1 Training Data

  • Training uses 1.20M image-editing pairs, 2.45M video-editing pairs, and 1.63M V↔I↔I chain triplets.The chain data is constructed from raw videos without using their paired video-editing annotations; V2V* pairs are reformatted into decomposed sub-chains.Source: §C.1 · pp. 22–23

C.2 Benchmark Details

  • OpenVE-Bench contains 431 clips across eight editing categories, scored on instruction compliance, consistency and detail fidelity, and visual quality and stability.The benchmark reports category-level and overall scores using an MLLM judge on a 1–5 scale.Source: §C.2 · p. 23

C.3 Implementation Details

  • Table 3 consolidates VINCIE-NExT’s architecture, data, training, and inference settings.The training and inference launch scripts and full hyperparameter configurations are provided in the code repository.Source: §C.3 · p. 24, Table 3 · p. 24

D.1 Performance Comparison Evaluated by Seed1.6-VL

  • 3.09 overall score on Seed1.6-VL evaluation places VINCIE-NExT above all open-source baselines and 0.41 points from Runway Aleph.Global Style scores 4.25 and Creative Edit 3.39, while Camera Edit remains the primary weakness at 2.01.Source: §D.1 · p. 24, Table 4 · p. 25

D.2 TDF3D-RoPE and Chain-of-Editing

  • TDF3D-RoPE alone yields a larger standalone gain than Chain-of-Editing alone, while their combined gain is not fully additive.The encoding helps most on fine-grained localization tasks; Camera Edit remains weak across configurations.Source: §D.2 · p. 25, Table 5 · p. 25

D.3 Temporal Position Randomization

  • Temporal Position Randomization is a prerequisite for Chain-of-Editing: without it, the edited keyframe can be treated as a structural cue instead of an appearance anchor.Randomizing image temporal positions encourages correspondence to be grounded in spatial content, with stronger combined benefits on Local Remove and Global Style.Source: §D.3 · pp. 25–26, Table 6 · p. 26

D.4 Data Composition and Image Editing Quality

The data-composition ablation on GEdit-Bench separates semantic instruction fidelity from perceptual quality: composition changes SQ substantially while PQ stays stable. The V↔I↔I chain is strongest as a single source, and combining it with paired data yields the largest SQ gain.

D.4 Data Composition and Image Editing Quality

  • Tab. 7 shows that SQ varies significantly across data configurations while PQ remains stable.The ablation reports SQ (Semantic Consistency), PQ (Perceptual Quality), and Overall Score on GEdit-Bench.Source: §D.4 · p. 26, Table 7 · p. 26
  • V↔I↔I yields the strongest single-source SQ, with its chain structure requiring an explicit semantic commitment before video generation.Source: §D.4 · p. 26
  • Adding V↔I↔I to I2I+V2V* produces the largest SQ gain, indicating that paired data and chain data play orthogonal roles.Source: §D.4 · p. 26

D.5 Comparison with Pure V2V Training

On OpenVE-Bench, decomposition and Chain-of-Editing outperform a same-backbone Pure V2V model despite using less paired video data. Image-level data and the in-context demonstration provide transfer, but the final I→V stage still requires video data.

D.5 Comparison with Pure V2V Training

  • Full data + CoE scores 2.65 overall versus 2.32 for Pure V2V, a +14.2% overall gain; its Creative Edit score is 2.89 versus 2.31, a +74.1% gain.The Creative Edit gain occurs despite zero V2V supervision for that category.Source: Table 9 · p. 27, §D.5 · p. 27
  • With the same backbone and reduced V2V data, Pure V2V scores 2.32 versus OpenVE-Edit's 2.49, while decomposition and CoE raise the score to 2.57.The Pure V2V comparison removes decomposition, in-context formulation, and CoE; OpenVE-Edit uses a strict superset of the paired video split.Source: §D.5 · pp. 26–27
  • The gains come from I2I and V↔I↔I data enabling implicit transfer and CoE's in-context edited-image demonstration providing explicit transfer.Source: §D.5 · p. 27
  • Adding 2.83M image-level samples increases visual training tokens by only ∼6%, but video data remains necessary to learn the final I→V step.The authors report a 14% overall gain and a > 2× efficiency ratio from those additions.Source: §D.5 · p. 27

D.6 Human Evaluation

A blinded pairwise evaluation of 431 OpenVE-Bench samples compared V2V*-only + CoE with full data + CoE. Raters favored the full-data configuration across dimensions, with substantial agreement, while ties were especially common for visual quality and consistency.

D.6 Human Evaluation

  • Ten raters each annotated all 431 samples in a blinded, randomized side-by-side comparison, selecting Video 1 better, Video 2 better, or Tie.Source: §D.6 · p. 27, Table 10 · p. 27
  • B wins more often than A on all three human-evaluation dimensions, most clearly for Prompt Following and Consistency.A is V2V*-only + CoE; B is full data + CoE, evaluated over 431 samples.Source: §D.6 · p. 27, Table 10 · p. 27
  • Fleiss’ κ = 0.721 indicates substantial agreement, and mean pairwise agreement is 90.4%, while unanimous agreement is 59.3%.The authors note that the 1–5 rating scale makes absolute score differences narrow and that ties between close preferences are common.Source: §D.6 · p. 27, Table 11 · p. 27
  • Ties are more common for Visual Quality and Consistency because the foundation model is shared and failed or partial edits can leave unrelated regions unchanged.The authors also note that instruction-following judgments are usually less ambiguous than subtle drift or per-frame artifacts.Source: §D.6 · p. 27

D.7 Evaluation with Objective VBench Metrics

The paper reports objective quality, test-time scaling, and latency results alongside a comparison with AnyV2V. VINCIE-NExT scores best on both VBench metrics, improves monotonically with more intermediate-image denoising steps, and is faster than AnyV2V by more than an order of magnitude.

D.7 Evaluation with Objective VBench Metrics

  • The objective VBench measures capture output visual and temporal quality but do not measure instruction compliance or preservation of unedited content.Source: §D.7 · p. 28
  • VINCIE-NExT achieves the best scores among recent baselines on both VBench metrics, with imaging_quality +0.027 over ICVE and quality_avg on par with or slightly above DITTO.VBench metrics assess visual and temporal output quality, not instruction following or preservation of unedited content.Source: Table 12 · p. 28, §D.7 · p. 28

D.8 Test-time Scaling and Efficiency

  • VINCIE-NExT improves from 2.30 to 2.81 overall (+22.2% relative) as intermediate image-editing denoising steps increase from 1 to 64, with gains across reported categories.The trend is measured with Gemini 3.1 Flash Lite; its absolute scores are not directly comparable with Gemini 2.5 Pro results elsewhere in the paper.Source: Table 13 · p. 28, §D.8 · p. 28, Figure 9 · p. 29
  • On one H200 GPU with 50 denoising steps, VINCIE-NExT is faster than ICVE, comparable to Lucy-Edit, and over an order of magnitude faster than AnyV2V.Source: §D.8 · p. 28, Table 14 · p. 29

D.9 Comparison with AnyV2V

  • Both VINCIE-NExT checkpoints outperform AnyV2V by a large margin, while AnyV2V is over 13× slower.These results use Gemini 3.0 Flash Lite and are not directly comparable with Tab. 1.Source: Table 15 · p. 29, §D.9 · p. 29

E More Qualitative Results

The qualitative results compare VINCIE-NExT with open-source baselines across editing categories and contrast direct video inference with Chain-of-Editing variants. The section also outlines the framework’s future directions, limitations, scope, and broader impact.

E More Qualitative Results

  • Figures 10–16 compare VINCIE-NExT with seven open-source baselines across seven editing categories, while Figure 17 contrasts direct inference and two CoE variants.The comparisons are presented as extended qualitative results.Source: E More Qualitative Results · p. 29

E.1 Qualitative Comparison between Baselines and Ours

  • Across spatially aligned edits, VINCIE-NExT follows instructions while preserving untouched regions and temporal coherence; for creative edits, its image-domain blueprint supports semantic reinterpretation.Baselines often omit edits or cause them to bleed into unrelated regions and flicker.Source: §E.1 · pp. 29–30

E.2 Qualitative Comparison between V2V and CoE Variants of VINCIE-NExT

  • CoE’s edited-keyframe stage yields sharper boundaries and more stable motion than direct V2V inference on subject replacement and removal; an external editor further improves semantic accuracy, especially for replacement.The external-editor substitution requires no retraining through the modular I→I interface.Source: §E.2 · p. 30

Future Work

  • Future work proposes a unified multimodal model that reasons about source videos and an agentic CoE framework for planning edits, invoking tools, and retrieving information.The proposed tools include segmentation, image editors, and quality verifiers.Source: Future Work · p. 30

Limitations

  • Video quality depends on the intermediate edited image, whose errors can propagate to every output frame; a single keyframe may also be inadequate for substantial temporal variation or camera motion.The paper also notes two sequential diffusion stages add latency and that evaluation is limited to OpenVE-Bench.Source: Limitations · p. 30

Scope

  • The framework targets instruction-driven appearance edits in short clips, assumes a static or slowly varying camera, and is not designed for physical simulation or long-range temporal reasoning.Rapid cuts, severe occlusion, and crafting demonstrations for highly complex edits may challenge its use.Source: Scope · p. 30

Broader Impact

  • The authors say the framework may make high-quality editing more accessible to individual creators and small studios, while warning of deceptive or non-consensual misuse.They encourage provenance and detection tools and endorse responsible-disclosure norms for consumer-facing deployment.Source: Broader Impact · pp. 30–31
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