VINCIE: Unlocking In-context Image Editing from Video
Authors: Leigang Qu
Feng Cheng
Ziyan Yang
Qi Zhao
Shanchuan Lin
Yichun Shi
Yicong Li
Wenjie Wang
Tat-Seng Chua
Lu Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: Image Editing
Video Generation
Diffusion Model
Primary area: generative models
TL;DR: We learn in-context image editing directly from videos using a block-wise causal diffusion transformer, achieving strong multi-turn image editing abilities.

Abstract: In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pipelines and expert models (e.g., segmentation and inpainting) to curate training data. In this work, we explore whether an in-context image editing model can be learned directly from videos. We introduce a scalable approach to annotate videos as interleaved multimodal sequences. 
To effectively learn from this data, we design three proxy tasks: next-image prediction, current segmentation prediction, and next-segmentation prediction. 
Additionally, we propose a novel multi-turn image editing benchmark to advance research in this area.
Extensive experiments demonstrate that our model exhibits strong in-context image editing capabilities and achieves state-of-the-art results on two multi-turn image editing benchmarks. Despite being trained exclusively on videos, our model also shows promising abilities in multi-concept composition, story generation, and chain-of-editing applications.

Published as a conference paper at ICLR 2026
VINCIE: UNLOCKING IN-CONTEXT IMAGE EDITING FROM VIDEO
Leigang Qu'
Feng Cheng" Yicong Li
Ziyan Yang² Qi Zhao?
Shanchuan Lin?
Yichun Shi
Wenjie Wang'
Tat-Seng Chua
Lu Jiang?
National University of Singapore
"ByteDance Seed
https://iiniie2255.ihhhe gittub..o.
(a) Multi-turn Image Editing
(b) Chain-of Edditing
(c) Story Generation
(d) Multi-concept Composition
BOFERII
Figure By learning from videos, our method could attain universal in-context editing and generation abilities to handle various practical creation scenarios.
ABSTRACT
In-context image editing aims modify images based contextual sequence comprising texts and images. Existing methods typically depend on task-specific pipelines and expert models (e.g., segmentation and inpainting) to curate training data. In this work, we explore whether an in-context image editing model can be learned directly from videos. Toward this end, we introduce a scalable approach to annotate videos interleaved multimodal sequences. To effectively learn from this data, we design three proxy tasks: next-image prediction, current segmentation prediction, and next-segmentation prediction. Additionally, we propose a novel multi-turn image editing benchmark to advance research in this area. Extensive experiments demonstrate that our model exhibits strong in-context image editing capabilities and achieves state-of-ther -hr-t-rrtttttttt results on two multi-tun turtiittuunnui image editing benchmarks. Despite being trained exclusively videos, model also shows

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promising abilities in multi-concept composition, story generation, and chain-of editing applications.
INTRODUCTION
Recent research has devoted significant effort to image editing, which enables users to generate images that closely follow editing instructions provided in text prompts. The performance of image editing models largely depends on the high-quality training data, typically composed of three elements: an input image, a text prompt describing the desired modification, and the corresponding edited image (Brooks et al., 2023; Shi et al., 2024a; Xiao et al., 2024; Wei al., 2024; Han et al., 2024b; Xia et al., 2024; Liu et al., 2025). To collect such paired image data at scale, various methods have been proposed, including generating image grids (Wu et al., 2025c). leveraging diffusion denoising processes (Brooks et al., 2023), and developing specialized models or tools to extract refore-and-afee image pairs from the web (Hertz et al., 2022; Zhuang et al., 2024; Boesel & Rombach, 2024).
Very recently, the problem of in-context image editing (OpenAI, 2025b) has garnered growing interest in the research community. In this setting, a target image is generated based on contextual sequence of text prompts and previously generated images. Unlike single-turn image editing, in-context image editing supports multi-turn interactions, enabling users to iteratively refine images while maintaining visual consistency throughout the editing process. A key challenge lies in acquiring contextualized training data that includes coherent sequences of text and images, Existing approaches to mine single-turn image editing (Brooks et al., 2023; Wu et al., 2025c; Hertz al., 2022; Zhuang et al., 2024; Boesel& & Rombach, 2024) struggle to construct meaningful long-form content that is capable of capturing the dependencies and evolving intent that emerge over multiple editing steps. The lack of contextualized, quality training data remains a significant barrier to progress in this area of research.
In this paper, we approach in-context image editing from different perspective and investigate the following research question: Can meaningful in-context image editing model be learned solely from videos, without using any standalone images? Our intuition is that videos, as a rich source of multimodal information, inherently contain a long duration of visual dynamics that might facilitate the learning of multi-turn interactions. For instance, changes within a scene, such as objects entering or exiting the frame, shifts in camera focus, or character actions, provide implicit cues for learning operations like addition, removal, and modification in image editing.
To this end, we propose approach that natively learns transitions from video data, named Video- driven IN-Context Image Editing (VINCIE). Unlike conventional image editing methods that rely on separately collected pairs of pre- and post-editing images for training, we choose not to alter the video, i.e., we train on native video data (only natural videos as the source of visual modality), but instead provide the model with detailed annotations that describe the transitions actions occurring within the scene. Since our method eliminates the need for paired data collection and relies solely on video, it can be trivially scaled using the vast amount of video data readily available on the web.
Specifically, we first sample a few coherent frames from a video scene, annotate the visual transitions, and identify Regions of Interest for editing (RoEs) using a pretrained Vision-Language Model (VLM). Additionally, we employ Grounding-Diin -INO (Liu et al., 2024b) and SAM2 (Ravi et al., 2024) to generate RoE segmentation masks based on textual descriptions of the transitions. This process establishes our training samples, which capture context and form an interleaved multimodal sequence. Next, we train a Diffusion Transformer (Peebles & Xie, 2023) with full attention as our primary implementation and additionally design variant with block wise causal attention, which applies bidirectional attention within each modality (frame, text, and segmentation mask) and causal attention across modalities. Both variants are compared to provide direct assessment of their differences.
Finally, to enhance the model's learning of contextual dependencies, we design three proxy tasks: (I) next-image prediction, which serves the primary task in training; (2) current segmentation prediction, which enables the model to understand which regions have changed; and (3) next segmentation prediction, which prepares the model to anticipate where changes are likely occur.
Extensive experiments show that our model, trained solely on video data, demonstrates strong in- context image editing capabilities and outperforms existing baselines the multi-turn image editing tasks. Scaling up the model and training data leads to substantial performance gains- cains--orr example, the success rate at the challenging 5-turn editing increases from 5% to 22% when scaling the training
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data from 0.25M to 10M sessions demonstrating the scalability of our approach enabled by native video data. Notably, to the best of our knowledge, this is the first work to demonstrate the feasibility of learning in- in-context image editing model solely from video data, while also showcasing the scalability benefits of this approach.
We find that our model can learn disentangled representations of visual changes (e.g., object appear- ance/disappearracee, posture shifts, and orientation changes) purely from patterns inherent in video data. It also demonstrates reasonable generalization scenarios that are less common in natural video, such as background changes, attribute modifications, and multi-concept compositions. As an additional benefit, our model can be used for generating consistent frames for storytelling through in-context editing.
2 RELATED WORK
Data Construction Methods for Image Editing. Constructing image editing datasets requires first designing clear and diverse editing instructions that articulate the intended visual modifications. Based on these instructions, paired image examples are then created, consisting of original images and their corresponding edited versions that reflect the specified transformations. Single-turn image editing methods (Hertz et al., 2022; Brooks al., 2023; Sheynin et al., 2024; Shi al., 2024a; Zhao et al., 2024; Wei et al., 2024; Hui et al., 2024; Yang et al., 2024b; Jin et al., 2024) use pre-trained off- the-shelf models (Ramesh et al., 2022; Rombach al., 2022; Brown et al., 2020; Sauer al., 2024) to construct paired data for image editing. For example, InstructPix2Pix (Brooks et al., 2023) leverages GPT-3 (Brown al., 2020) for generating editing instructions and Stable Diffusion v1.5 (Rombach et al., 2022) for paired image data generation. UltraEdit creates editing instructions using LLMs and combines grounding models (Kirillov al., 2023; Liu et al., 2024b) with SDXL-Turbh Turbo (Sauer et al., 2024) to produce region-basse editing samples. Our approach relies on learning transitions from videos without manually crafted paired data pipelines, bringing impressive scalability.
Learning from Video for Image Generation. Video Frames naturally exhibit consistency across characters, objects, and scenes, which has inspired recent efforts to construct source and target images from sampled video frames. Leveraging such trame-based data has proven beneficial for enhancing consistency in image generation tasks, such as instructive image editing (Chen et al., 2024d; Krojer al., 2024), interactive image editing (Zhang al., 2025a; Shi al., 2024b), streamlining image editing (Alzayer et al., 2024), and object-level image customization (Chen et al., 2024c). The most recent work, e.g., RealGeneral (Lin et al., 2025) and UES (Chen al., 2024a), explored the temporal in-context consistency within video foundation models (Yang et al., 2024c) for universal image generation and editing. Despite notable progress, existing methods typically rely on only two frames per video, overlooking richer, long-range contextual information. Furthermore, they often depend on task-specific data construction pipelines (Chen et al., 2024d; Zhang al., 2025a; Chen al., 2024c), limiting their universality and scalability. In this work, we propose constructing session-wise data with long, interleaved image-text context from native videos, and leverage for pre-training or mid-training to learn the inherent consistency and transformations in abundant multimodal sequences.
3 METHODOLOGY
3.1 INTERLEAVED MULTIMODAL SEQUENCE CONSTRUCTION
Figure 2 shows an overview of our data construction pipeline. Starting with video, we sparsely sample K frames (Jo..., Ix) "o) transitions T; describing the change from frame I, to T++1- To better capture the Regions-of-interee for editing (RoEs),
and use a vision-language model (VLM) to generate textual visual
additionally annotate segmentation masks M, and M,+1, which identify the changing objects in 1, and 1,+1, respectively. Combining these elements, we construct the multimodal sequence (Io, To. Two,, Moo. Tma, Mo1, I1, J1,..,, Ik). Tio and T,1 are predefined prompts such as "generate the mask of changing areas in the source image" and "generate the mask of changing areas in the target image".
Frame Sampling. We use a hybrid sampling strategy: 1) Equal-intens interval sampling, which selects frames at fixed time intervals (e.g., 3 sec), and 2) Fixed-frame sampling, which uniformly samples
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"source caption"
Large Multimo Models (Laooda
"ccene: table with two cups of coffee, bouquet summer floeers,
angion-of-Editing
Fused Global RoE
Frame Sampling
(RoE)
"cbll" bouquet summer flowers with riobons"
NUI
two coffee each
Grounding SAM2
cups bouquet souquet flowers
50
ohiz": two cups coffee each
spoor"]
"ojjcct change"
obji: change".
07
bbk": move lhe lao cupa of coffee slubily buther
2ear"1
"smmnary_ change": "Remove the hands holding the
cups and move the cups siihtyyapp apart."
Region-level descriptions
Figure 2: Our session data construction pipeline. We use a VLM to annotate the visual transitions.
We then use the generated textual descriptions prompt GroundingDIN++AAM2, extracting seg- mentation masks for the edited regions.
fixed number (e.g.. 2 S 6) of frames regardless of video duration. This approach is used to capture both subtle object-level changes and significant scene-level transitions.
Visual Transition Annotation. To describe visual transitions between frames, annotation: thought (CoT)) prompting (Wei et al., 2022) to instruct a VL.M to perform visual transition 1) generate detailed and coherent descriptions of each frame from multiple aspects (e.g., characters, objects, attributes, interactions, scenes, and environments): 2) identify semantic and visual differences between the two frames from the above aspects; 3) and summarize all the differences into concise, instruction-style statement 7; suitable for guiding editing. Unlike existing interleaved datasets (Zhu al., 2023; Laurençon al., 2023; Chen al., 2024b) derived from web documents and retrieval tools, our dataset is built from native videos, ensuring stronger textual and visual coherence.
chain-of-
Segmentation Annotation and Encoding. We explicitly annotate Regions-offEditing (RoEs) in both adjacent frames I; and T++1- Specifically, we leverage region-level descriptions (i.e., characters and objects) in the visual transition annotation as input to GroundingDINO (Liu al., 2024b) and SAM 2 (Ravi al., 2024) for extracting segmentation map's. Based the region-level difference annotations, we determine which regions undergo visual transitions, i.e., RoEs, and construct corresponding global maps by fusing local maps from the current and next session images.
3.2 MODEL ARCHITECTURE
As illustrated in Fig. 3, our model is built upon a Diffusion Transformer (DiT) architecture, initialized from video foundation model. We represent the interleaved input sequence as S (Io, .g...., -M-1: IM), where T; denotes the textual editing instruction at turn-i, and I represents either an image or a segmentation mask.
As our focus is on the in-context image editing task, we optimize the model by maximizing the likelihood of the next image prediction:
M
logp(S) Σ
log p(I Io...., T T,,1,
(I)
where the conditional probability is modeled using flow-matching in the latent space, an objective commonly used in diffusion model for text-to-image (Rombach et al., 2022; Esser et al., 2024; Labs, 2024; Podell et al., 2023) and text-to-video (Singer et al., 2022; Wan et al., 2025; Hong et al., 2022; Seawead et al., 2025; Qu et al., 2025c) generation tasks. Each text instruction (T,) and image (I)) is encoded into latent tokens using text encoder (e.g., T5) and an image encoder (e.g., VAE), respectively. The details about the text encoder and VAE provided in the supplementary material.
Learnable <TURN> Tokens. We separate the interleaved input sequence S by modality into two groups: S (To, To...., ..., Tas-1, JAS) T (To, T....., T TM--1 1); (Jo,.., TM). Their latent
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Next Image Prediction (NIP)
Current Seg. Prediction (CSP)
Next Seg. Prediction (NSP)
NIP
Diffusion Transformer with Full Attention (initialized from a Video Foundation Model)
noised
hoised
noised
noised
TLAN
TUAN
UAE S Encosder
VAE S Encoder
VIKE Encoder
Encoder
ViEE Encoeeer
T5 Encoder
VIKE Encoder
Remove the hands a move the cups slightly oport.
Add two hands reaching towards the cups.
Context
Composition
Figure 3: Model architecture. We apply a diffusion transformer framework (initialized from video generative foundation model) with full attention learn from the multimodal interleaved context, through three tasks (CSP, NSP, and NIP). Losses are only computed on noised tokens.
tokens are concatenated together. Since the number of text tokens at each turn may vary, we introduce M special learnable tokens <TURN>;, i = 1.... M to mark the turn boundary, where <TURN>; is inserted before the latent tokens of T,
Separate Text and Image Position Embedding. We apply ID RoPE (Su et al., 2024) to text tokens and 3D RoPE image tokens. The starting positions are for all dimensions. This separate RoPE design aligns with our pretrained MM-DiT model, where text and image tokens are positioned continuously. Position collisions are avoided as MM-DiT employs distinct weights for each modality, and the bias terms in the linear layers effectively act modality-speciff embeddings.
Attention. We employ two attention mechanisms in DiT and obtain two variants: (1) full attention over all tokens, shown in Fig. 3, and (2) block-wiss causal attention, where causality is enforced across blocks (e.g., text or image) and bidirectional attention is applied within each block. Full attention enables comprehensive token interactions higher computational cost, while biock-wise causal attention improves efficiency while maintaining causal structure. Additional details and discussions are provided in Appendix C.4.
Condition on Clean Context. We model the distribution of each image (except the first) using diffusion loss, conditioned on interleaved context. To enhance training efficiency, we concatenate the clean and noisy tokens of each image as model inputs, and apply an attention mask to ensure that each noisy image attends only the clean representations of preceding images, as illustrated in Fig. 11.
3.3 CONTEXT COMPOSITION LEARNING
To facilitate effective ability transfer from segmentation modeling to image editing and generation, we unify image and segmentation modeling within a generative framework using the MSE-based diffusion loss in flow matching. Through interleaved context composition, our framework further unlocks multiple capabilities and supports variety of corresponding tasks (see Fig. 12 for more details). Specifically, we augment Equ. by adding a random dropout operation Rd on the context, shown in equation:
log pgp(S) = ((())==oop logg(FF Rd(1o, Rd Moo), Rd(Tma Mo1)....)
where F; can be either the target image, RoE mask' of the source image, RoE mask of the target image. We ensure that the image or mask required to generate the target is always retained, while only the contextual images and texts randomly dropped. The model is jointly learning three tasks:
In implementation, treat segmentation masks RGB images, by replicating the mask across all three channels, and then encode them using the VAF encoder obtain the latents.
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Next Image Prediction (NIP). NIP is primary in-context image editing task.
Current Segmentation Prediction (CSP). CSP enhances the model's grounding ability, enabling it to identify regions requiring edits while preserving consistency in other areas. This is particularly useful for local editing tasks such as removal, attribute changes, and replacements.
Next Segmentation Prediction (NSP). NSP improves the model's controllable generation by incorporating the next-frame segmentation map into the context, aiding in dynamic layout adjust- ments for scenarios like shape changes and movements.
By randomly combining different contexts and tasks, the model learns essential abilities such as grounding, controllable generation, and multi-concept composition, enabling versatile in-context image editing.
4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Data. Through the proposed scalable data construction pipeline, collect and annotate about 10M session instances, with the number of images in each session from 20. For each session data, we consider RoE map with probability of 80%. We apply a context drop rate with 20%, 70%, and 70%, to the current frame, current RoE map, and next RoE map. respectively. During inference, the sampling step is construction pipeline, we collect and annotate about 10M session instances, each containing 2 to 20 images. During training, RoE map included with an 80% probability for each session. We apply context dropout rates of 20%, 70%, and 70% to the current frame, current RoE map, and next RoE map, respectively, with dropout applied independently at each turn. We use 50 sampling steps and set the classifier-free guidance scale to 10.
50, the classifier-free guidance scale is
10. Using the proposed data
Model. We initialize our model with the weights of our in-house MM-DiT (3B and 7B), pre-trained on text-to-video tasks and architecturally similar to (Seawead et al., 2025; Kong et al., 2024). The 3B and 7B variants are optimized on session data for 15k and 40k steps, consuming approximately 30 and 150 hours on 256 H100 GPUs, respectively.
Add
Attribute
Global
4.2 MULTI-TURN
Interaction
SESSION IMAGE EDITING BENCHMARK
Local
Remove
Existing benchmarks (Zhang et al., 2023a; Basu et al., 2023; Sheynin al., 2024), such as Mag- icBrush (Zhang et al., 2023a), are constrained to basic editing operations, such addition, replace- ment, removal, attribute modification, and back- ground changes, and thus fall short of meeting prac- tical user needs. Moreover, MagicBrush supports only up to three editing turns per session, with each turn treated in isolation, further diverging from real- world editing workflows. To address these limita- tions, we propose MSE-Bench (Multi- turli--turnnn Session image Editing Benchmark), which comprises 100 test instances, each featuring a coherent five-turn editing session. MSE-Bench expands the range of editing categories to include more complex and realistic scenarios such as posture adjustment, object interaction, and camera view changes, as shown in Fig. 4. To better reflect user intent and practical applications, we also incorporate aesthetic considerations into the construction, encouraging progressive visual enhancement across turns.
Camera
MSE-Bench
Action
Cheracter
Orientation
Replace
Expression
Position
Pasture-
Background
Figure 4: Category distribution of MSE-Bench. "others" includes expression, orientation, posi- tion, global, and action change.
For each editing instruction, multiple generated images may satisfy the user's 's request. Consequently, benchmark does not provide ground-truth images. Instead, the generated image successfully follows the instructions and remains consistent with the input image. The final score for each turn computed by averaging the success rates across all samples.
GPT-4o evaluate whether

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Table 1: Performance comparison on MagicBrush (Zhang et al., 2023a) (multi-turn) for consistency
(DINO and CLIP-I) and prompt following (C. SFT means we carry out supervised fine-tuning.
* denotes the use of context across all preceding turns. Entries by gray denote proprietary models.
Turn-I CLIP-1
Turn-2
Trun-3
Method
DINO
CLIP-T
DINO
CLIP-1
CLIP-T
DINO
CLIP-1
CLIP-T
Prstucc-Pix22ixx (Brooks et al., 2023)
0.514
0.727
0.270
0.397
0.674
0.268
0.335
0.646
0.263
MagicBrush (Zhang al., 2023a)
0.826
0.901
0.278
0.756
0.863
0.277
0.718
0.834
0.271
HQUdit (Hui et al., 2024)
0.522
0.696
0.259
0,441
0.659
0.248
0.397
0.637
0.238
UltrEdit (Zhao al., 2024)
0.755
0.852
0.289
0.706
0.827
0.278
0.683
0.810
0.266
ICEdit (Zhang et al., 20256)
0,853
0.922
0.281
0.780
0.882
0.278
0.731
0.852
0.272
OmniGen (Zhing et al,, 20256)
0.874
0.924
0.273
0.718
0.851
0.264
0.586
0.786
0.261
OmniGen2 (Wu
2025h)
0.863
0.919
0.285
0.777
0.832
0.278
SteplX-Edit (Liu et al., 2025)
0,852
0.915
0,288
0.785
0.875
0,286
0.743
0,840
0.277
Bugel (Deng et al., 2025)
0.845
0.912
0,286
0.767
0.873
0.292
0.723
0.844
0.286
Bagel* (Deng
2025)
0.847
0.914
0.729
0,245
0.287
FLUX. 1-XX.-Konie K-nntet (dev) (Batifol et al., 2025)
0,858
0.917
0,288
0.757
0.863
0.296
0,691
0,818
0.291
Qwen-Image-BB Edit (Wu et al., 2025a)
0.827
0.900
0.292
0.745
0.856
0.292
0.697
0.819
0.287
GPT Image (OpenAl. 2025a)
0.805
0.875
0.293
0,708
0.820
0,300
0.666
0.789
0.292
Nano Banana (DeepMind & Gemini. 2025)
0,886
0.933
0.287
0.811
0.896
0.294
0.773
0,867
0.291
Ours* (3B) Ours* (3B)+ + SFT
0.822
0.895
0.273
0.733
0.850
0.272
0.676
0.827
0.267
0.852
0.917
0.283
0.739
0.861
0.291
0.667
0.814
0.290
Ours (7B) Ours* (7B) + SFT
0.838
0.906
0.272
0.721
0.848
0,272
0.645
0.804
0.271
0.891
0.937
0.283
0.817
0.895
0,289
0.775
0.861
0.286
4.3 COMPARISON WITH SEATE-OF-THE--O
We evaluate our model on two multi-turn image editing benchmarks: MagicBrush (Zhang et al., 2023a) and our proposed MSE-Bench.
MagicBrush. Given its support for multi-turn editing, high-quality manual annotations, and close alignment with real-world editing needs, we first adopt MagicBrush evaluate our method and compare against baselines.
Tab. 1 reports quantitative results across three standard evaluation metries: DINO, CLIP-I, and CLIP-T. First, our model, trained solely on interleaved video data, achieves performance comparable to SOTA methods UltraEdit and OmniGen, which rely on pairwise editing data, highlighting video data as a natural and effective source for image editing tasks. Second, with supervised fine-tuning on editing-orienter data, our method outperforms nearly all metrics, demonstrating that interleaved video data complements existing data creation approaches. Lastly, our model's ss advantages become increasingly evident with more edit turns, showcasing the benefits of learning from contextual video data.
MSE-Bench. Tab. 2 presents the multi-turn editing success rates as evaluated by GPT-4o. In this setup, the generated image at turn-i serves as the imput for editing at turn- + 1. Consequently, failure at any turn propagates to subsequent turns. Existing academic methods perform poorly, with success rate of 2 % at turn-5. In contrast, our method achieves 25% success rate at turn-5, demonstrating the advantages of our model and the use of native video data. However, our approach still falls short compared to proprietary models like GPT-4o, which benefit from significantly larger training datasets and model sizes. Even so, GPT-4o achieves only 62.7 % success rate, highlighting the long-term value of our proposed benchmark for advancing multi-turn tulti-tuunne editing.
4.4 IN-DEPTH ANALYSIS
In-Context Editing Mitigates Artifact A Accumulation. Artifact accumulation, where artifacts be- come more pronounced with increasing editing turns, is a common issue in multi-turn editing (Sheynin et al., 2024). We observe this phenomenon well (upper part of Fig. 6) when using model as single-turn editing method, i.e., without incorporating context from previous turns. However, when all contexts are included as input, no artifacts observed (lower part of Fig. 6).
Impact of Segmentation Prediction and Generation. As shown in Tab. 3, training with seg- mentation and generation context enhances both consistency and multi-turn editing success rate. Notably, the substantial gain in consistency on MagicBrush (Zhang et al., 2023a) demonstrates the effectiveness of segmentation modeling, especially under the CoB strategy (CS NS + I).
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Table 2: Performance comparison on MSE-Bench (editing success rate evaluated by GPT-4o). *
denotes the use of context across all preceding turns. Entries by gray denote proprietary models.
GPT-4o Evaluation Turrn33
Method
Turn-
Turn-2
Tumn4
Turn-5
Iostruct-Pix22 (Brooks 2023)
0.520
0.130
0.110
0.083
0.060
MagieBrush (Zhang et al., 2023a)
0.707
0.300
0.213
0.170
0.087
HQEdit (Hui et al,, 2024)
0.477
0.177
0.140
0.113
0.077
UltraEdit (Zhao et al., 2024)
0.673
0.230
0.173
0.113
0.067
ICEdit (Zhang et al., 2025b)
0.633
0,340
0.257
0.163
0.090
OmniCinn (Xiao et al, 2024)
0.223
0.140
OmmiGen (Xiao al., 2024)
0.853
0.188
0.160
0.125
0.065
OmniGen2 (Wu et ul., 20256)
0.847
0.393
0,327
0.263
0,133
Step Ed-iit (Liu ct al., 2025)
0.937
0.540
0.420
0,300
0,140
Bagel (Deng al., 2025)
0.967
0,650
0.613
0,550
0.413
Bagel* (Deng et al., 2025)
0,630
0,473
0,300
FLUX.I-Kontext (dev) (Batifol et al., 2025)
0,950
0.670
0.623
0,573
0,440
Qwen-Image- Edit (Wu al., 2025a)
0,980
0.737
0.667
0.613
0.430
GPT Image (OpenAI, 2025a)
0.963
0.690
0.673
0.637
0.557
GPT Image 1 (OpenAI, 2025a)
0.967
0.707
0,700
0.697
0.640
Nano Banana (DeepMind Gemini, 2025)
0.987
0.773
0.753
0,727
0.627
Nano Banana (DeepMind Gemini, 2025)
0.997
0.773
0.757
0.730
0.643
Ours (3B)
0,913
0.450
0.393
0,300
0.210
Ours (3B) + +SF
0.913
0.533
0.497
0,443
0,330
Ours* (7B)
0.837
0.517
0.463
0.400
0.350
Ours* SFT
0.950
0.693
0.667
0.617
0.487
Table 3: Impact of segmentation (seg.) prediction and generation as context during training and inference on consistency (CLIP-I and DINO on MagicBrush) and success rate (evaluated by GPT-4o). image generation. CS: current segmentation generation. NS: next segmentation generation. (This ablation study was conducted using an intermediate checkpoint, so the reported numbers may not be directly comparable to those in other tables.)
MagicBrush (CLIP-I)
MagicBrush (DINO)
MSE-Bench (Success Rate by GPT-4o)
Train
Inference
Turn1
Turn-2
Turn-3
Turn-1
Tumn-2
Turm3
Turn-1
Turn-2
Turn-3
Turn-4
Turn-5
w/o Seg.
0.875
0.824
0.784
0.765
0.663
0.592
0.847
0.473
0.337
0.177
0.113
Seg.
0.797
0.786
0.604
0.887
0.520
0.103
Seg. CS
0.886
0.832
0.801
0.797
0.687
0.622
0.873
0.590
0.407
0.260
0,173
w/ Seg. NS
01889
0.840
0.815
0.807
0.711
0.661
0.837
0.487
0.323
0.197
0.117
w/ Seg. CS
NS I
0.890
0.847
0.823
0.814
0.724
0.679
0.867
0.523
0.367
0.190
0.110
Impact of Context. Table highlights the impact of context multi-turn image editing. In Turn11, where no prior con-
text exists, adding a dummy con- text-comprising the original image and an instruction, In Turn-2 and Turn33, where editing instructions and ground-truth images from pre- vious turns are provided as context, adding dummy context results in minimal improvements. "generate the same image." prepended before siini-iign improves per- formance. The L1 and L2 distances are nearly halved, indicating greater consistency between the generated image and the original image in un- changed areas, as these distances are measured pixel-wise. This is ex- pected, the existing context already provides sufficient information. These
Table 4: Impact of context on multi-turn image editing with MagicBrush. The "Dummy-Context" includes the orig- inal image and the instruction, "generate the same image." "History" refers providing previous turns' ground-trute -outh ages as context. Results show that performance significantly improves when a reasonable context is included, emphasiz- ing the importance of context in multi-turn image editing.
Method
L1j
DINO
CLIP-II
CLIP-T
Tum-1
w/o Context
0.155
0.063
0.814
0.894
0.277
Dummy Context
0.086
0.031
0.850
0.913
0.277
Tum-2
w/o Context
0.159
0.067
0.834
0.902
0.279
History
0.099
0.038
0.845
0.909
0.278
Dummy- Context
0.087
0.033
0.869
0.922
0.280
Tum-3
w/o Context
0.164
0.071
0.851
0,904
0.273
History
0.088
0.034
0.878
0.923
0.273
Dummy- Dummy-Content
0.088
0.034
0.895
0.929
0.272
findings underscore the critical role of context in multi-turn image editing tasks.
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Sequential Single-Turn Editing
Without Segmentation Mask Prediction
Input Image
Input Image
With Segmentation Mask Prediction
In- In-Context Editing
Figure 7: Subject position shift can be addressed by predicting segmentation mask first.
Figure 6: In-context editing mitigates artifact ac- cumulation issue in sequential single-turn editing.
Tum-1
Tum-2
Turn-3
Scalability. Fig. 5 illustrates the editing success rate as a function of training data size. While the success rate at Turn-1 begins to saturate at 2.5M training samples, the success rate at later turns (e.g., Turn-4 and Turn-5) exhibits nearly log-linear increase with more training data. These results demonstrate the scalability of both our model and data construction pipeline.
Tum-4
Tum-5
Success Rate by GPT-4o
1.0
0.8
0.6
0.4
0.2
Training on Native Video Data Introduces Addressable Subject Position-Shift. A key challenge when training on videos the potential for subject position shifts across editing turns, illustrated in the upper part of Fig.7. This issue arises from the natural movement of subjects over time in videos. However, incorporating segmentation pre- diction-wweet the model first predicts mask before generating the target image-mittee mask enforces consistency in unedited regions, thereby reducing positional drift.
0.0
0.25
1.25
(M)
Data Amount (log, scale)
Figure 5: Editing success rates in turns various data scales.
this drifting effect (see lower part of Fig.7). The segmentation
Effectiveness of Our Video Sequence Data. Table 5 demonstrates the impact of incorporating our video sequence data. Us- ing the same pretrained model, training with our video sequence data increases success rates by 16.4% and 21.0% on Turn-1 and Turn-5, respectively, compared to training solely on specialized pairwise image editing data (Wei et al., 2024). The highest perfor- mance achieved by first pretraining on our video sequence data, followed by supervised fine-tuning (SFT) on pairwise data, underscoring the effectiveness of our data for continual pretraining.
Table 5: Ablation study on MSE-Bench (GPT-4o eval- uated success ratc), to assess the impact of our video sequence data.
Training Data
Turn-
Turn-2
Tum-3
Tum-4
Turn-S
pairwise
0.723
0.263
0.123
0.033
0.010
sequence
0,887
0.597
0.417
0.280
0.220
sequence
+ pairwise
0.880
0.647
0.483
0.370
0.250
4.5 APPLICATIONS
Fig. showcases several emerging capabilities that arise when training our model exclusively on video data. Notably, these abilities seem to develop implicitly, as they differ from the model's explicit training objectives:
Controllable Editing:
By including the segmentation mask of the region of interest in the context, users can achieve controllable editing by modifying the segmentation mask.
Multi-Concept Composition: The model demonstrates the ability to compose multiple concepts together, even without explicit composition training data-a surprising emergent capability,
Story Generation: Leveraging the consistent and extended context in video data, the model can generate coherent frames for storytelling through in-context editing.
Chain-of-Be Chain-of-Ediiing: Each multi-turn editing session functions as a multimodal chain of thought, where the model interprets editing instructions, identifies regions of interest, generates Rol masks,

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Published as a conference paper at ICLR 2026
produces target images, and iterates the process. Our model reveals the potential of video data in modeling multimodal chains of thought.
5 CONCLUSION
In this work, we explore the research question: "Can an in-context image editing model be learned solely from videos?" To address this, we propose a learning framework that enables context-aware image generation directly from native videos. We introduce scalable data construction pipeline that transforms videos into contextual multimodal sequences, comprising sparsely sampled frames, textual visual transition descriptions, and segmentation masks of regions of interest. To model this multimodal sequence, we train DiT model using three proxy tasks: next-image prediction, current segmentation prediction, and sext-segmentttion prediction. Experimental results demonstrate that model, trained exclusively videos, exhibits strong in-context image editing capabilities and achieves state-of-the-as performance on multiple multi-turn image editing benchmarks. Additionally, model showeases emerging abilities such controllable editing, multi-concen composition, story generation, and multimodal thain-offtthuuht. highlighting the untapped potential of video data and the effectiveness of our proposed framework.
ETHICS STATEMENT
Our work scalable, context-aware image editing has the potential to democratize creative tools, enhance accessibility, streamline media production, and advance intuitive human-Al collaboration. However, it also raises important concerns, including the risk of misuse for misinformation or manipulation, privacy issues from large-scale video data, potential biases in generated content, job displacement in creative industries, and increased environmental impact due computational demands. Addressing these challenges will require careful dataset curation, privacy safeguards, bias mitigation, responsible deployment practices, and ongoing engagement with diverse stakeholders.
REPRODUCIBILITY STATEMENT
We have taken several steps to ensure the reproducibility of our work. A link (https: :// vincie2025, giitubu io/) the source code is provided, enabling replication of our im- plementation. The main text and appendix together provide comprehensive descriptions of the model design, training procedure, and evaluation protocol. Details on the dataset construction and preprocessing pipeline are presented in the appendix. These resources collectively ensure that readers can reproduce and validate our experimental results.
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Wanrong Zhu, Jack Hessel, Anas Awadalla, Samir Yitzhak Gadre, Jesse Dodge, Alex Fang, Youngjae Yu, Ludwig Schmidt, William Yang Wang, and Yejin Choi. Multimodal c4: An open, billion-scale corpus of images interleaved with text. Advances in Neural Information Processing Systems, 36: 8958-8974, 2023.
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CONTENTS
Introduction
2
Related Work
3 Methodology
3.1 Interleaved Multimodal Sequence Construction
3.2 Model Architecture
3.3
Context Composition Learning
Experiments
4.1
Implementation Details
4.2
Multi-Tun Session Image Editing Benchmark
4.3
Comparison with State-of-the-Arts
4.4
In-depth Analysis
4.5
Applications
5
Conclusion
10
Appendix
19
The Use of Large Language Models
21
B Additional Related Work
21
C Implementation Details
21
C.I Data Details
C.2 Visual Transition Annotation
23
C33 Segmentation Mask Annotation and RoE Construction
................................
23
C,4 Model Architecture
26
C.5 Composition of Input Conditions and Output
................................
26
C.6 Details of MSE-Bench
28
C.7 Supervised Fine-Tuning
28
D Additional Experimental Results
29
D.I Human Evaluation on Multi-turn Image Editing
29
D.2 Correlation Between GPT-4o and Human Evaluation
29
D.3 Human Evaluation for VLM annotation
29
D.4 Additional Ablation Study
29
D.5 Additional Performance Comparison on Story Keyframe Generation
30
Additional
Application Examples
32
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E.1
Image Editing
32
E.2
Multi-concept Composition
36
E.3
Story Generation
38
E,4
Chain-of-Editing
E.5
Drag-based Image Editing
41
Limitations
42
G Future Work
42
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A
THE USE OF LARGE LANGUAGE MODELS
We acknowledge that large language models (LLMs) were employed to assist in the preparation of this manuscript. Their use was restricted to grammar checking, language refnement, and enhancing clarity and fluency of the text. In addition, LLMs were applied in limited capacity support minor debugging and syntactic corrections of code snippets.
B ADDITIONAL RELATED WORK
Image Editing. Building on advances in foundational image generation models (Huang et al., 2025; Ramesh et al., 2022; Saharia et al., 2022; Esser et al., 2024), image editing has achieved remarkable progress. Techniques now enable a wide range of edits, including zero-shot editing (Li et al., 2024;
Huang et al., 2023; Wu & De Torre, 2023; Han et al., 2024a; Zhou et al., 2025b; Chen & Huang, 2023; Zhou al., 2025a), changing object classes (Kim al., 2022; Xu et al., 2023; Ackermann & Li, 2022; Yang et al., 2023c; Tsaban & Passos, 2023; Gholami & Xiao, 2023; Brack et al., 2024; Nie al., 2023) and faces (Ding al., 2023), free-form text- text-based modifications (Brooks al., 2023; Hertz et al., 2022; Lin et al., 2023; Dong et al., 2023; Zhang et al., 2023b; Kawar et al., 2023; Guo & Lin, 2024; Zhang al., 2024; Sheynin et al., 2024; Wei al., 2024; Shi al., 2024a; Wang et al., 2023a; Li et al., 2023; Mirzaei et al., 2024; Miyake et al., 2025), mask-based edits ( (Wang et al., 2023b; Xie al., 2023; Couairon et al., 2022: Zou al., 2024; Mao et al., 2024), point dragging (Mou et al.. 2023; Shin et al., 2024; Liu et al., 2024a; Lu et al., 2024: Choi et al., 2025), and reference image-guided transformations (Song et al., 2023; Goel et al., 2023; Yang et al., 2023a). series of recent works (Yang et al., 2023b; Wu et al., 2023; Xiao et al., 2024; Najdenkoska et al., 2024; Sun et al., 2024) enables edits conditioned on multiple text and images. Our work focuses on in-context image editing (OpenAI, 2025b), where edits conditioned on contextual sequence of text and previously generated images. Moreover, we explore learning from native video data, unlike existing methods that use hand-crafted synthesized data.
C IMPLEMENTATION DETAILS
C.1 DATA DETAILS
The training videos are sourced from a wide spectrum of domains, including stock footage, films, documentaries, etc. We split the raw videos into both single-shot clips and multi-shot scene videos. We also pre-process the raw videos by using different filtering strategies to keep high-quality videos, including logo detection, black border detection, and aesthetic estimation.
As described in Sec.3.1, we adopt two frame sampling strategies: equal-interval sampling and fixed-frame sampling. As illustrated in Fig.8, these approaches jointly ensure both the diversity and temporal stability of visual dynamics- two key factors for effective training of in-context image editing models.
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video
all video frames
sampled frames
Equal-interval Frame Sampling
video frames
all video frames
sampled frames
(b) Fixed-Frame Sampling
Figure 8: Two ways of frame sampling: (a) equal-interval sampling and (b) fixed-frame sampling.
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C.2 VISUAL TRANSITION ANNOTATION
Instruction for Visual Transition Annotation
Imagine that you are an image editing assistant who wants to edit the first image to the second image. I will provide you two frames from a video clip as the source and target images. The caption of the raw video clip is: (}
Your task is summarize how you intend achieve this image editing task by providing detailed but brief text instructions, from the following guidelines:
1. Understand the two images first, and describe the two frames in detail and coherently. Please include the details of the environment, main subjects, their appearances, and main features.
2. Describe the main characters and objects and their appearances. Do not mention the real name entities. Follow the format such as: ("charl": "a woman with hlonde hair wearing red jacket", "char2": "a girl wearing floral dress", "objl": "a green apple".... ...)
3. Highlight the semantic and visual differences between the two images in detail.
4. Provide only factual descriptive differences based on observable content. Avoid words phrases that suggest speculation or assumptions, such as "likely", "possibly", "appear to".. Avoid elliptical referential pronouns, such as "the same, frame frame the first image, the second image...."
An editing instruction should include:
main character change, including appearance, disappearance, position, action, expression, pose, orientation, ... (e.g. "make the person smile")
2. object change, including appearance, disappearance, position, count, relationship. layout, ... (e.g.. "add a dog beside the person")
attribute change, including color, texture, material, shape, size, depth, dynamics, ... (e.g., "make the person's hair red")
4. interaction change, including the interaction between characters, objects, and the environment. (e.g..
"make the person hold the dog"))
5. global change, including background, atmosphere, environment, style, weather, season, lighting,...
(e.g. "make the weather dark")
camera change, including orbiting, dolly-in, dolly-out, pan-left, pan-right, tilt-up, tilt-down. 7. others
Output Format: You should output a json file include the following information:
Framel Caption: <descrihe the first image/frame, characters and objects in detail> Frame2 Caption: <descrihe the second image/frame, characters and objects detail>
Character Change: <the detailed character and attribute change>
Object Change: <the detailed object and attribute change>
Global Change: <the detailed global change>
Camera Change: <the detailed camera change>
Other Change: <the detailed other change>
Summary Change: <a comprehensive but brief user editing instruction to achieve the editing>
Your output should be JSON file in one row (without any format), which looks like: ['framel caption": ["scene": str, "charl": str, "char2": str, ... "obj1": str, "obj2": str, ...). "frame2 caption": ("scene": str, "charl": str, "char2": str, ... "obji": str, "obj2": str, ...). "char- ["charl": str, "char2": str, ...). "object "object_ccanne": ["objl": str, "obj2": str, ...). "global_change". str, "camera_ "camera_change"" str, fother_changee str, hoummayy_changeee str)
acter
change": acter_change",
To bridge the semantic gap between two sampled frames,
our in-house LMM to annotate visual transitions, as introduced 1.c.3.1. The instruction used during annotation is shown above, and Fig. 10 presents example annotations to illustrate their quality.
C.3 SEGMENTATION MASK ANNOTATION AND ROE CONSTRUCTION
The proposed visual transition annotation framework leverages an LMM to generate multi-level annotations, ranging from local concepts to global scene descriptions. As illustrated in Fig.2, we first use character and object descriptions from the source and target frames query inputs to GroundingDINO(LLn et al., 2024b) to obtain object detection results. These detections are then passed SAM 2 (Ravi et al., 2024) to extract segmentation masks for the corresponding local concepts. Guided by the annotated local changes, we identify and fuse the objects or characters undergoing transitions construct the final RoEs.
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Replace the crescent maon stors with smiling sun to change the day to daytime.
Remove racky cliff mist, change background acean beach with a hazy sky. Swap the positions of women, them sideways, them face the camera more directly.
Turn Remove the racky cliff and mist, change the background ocean beach hazy sky. Swap the positions of the two women, let them stand sideways, comera more directly
Tum 1: Change the man's s facial expression from neutral to an open mouthed expression speaking excloiming
Tum Remove the man from the image and close the door.
them
Turn 1: Change the background landscope show more greenery, smaller water bodies, some buildings near the shoreline in the distance. Turn the man' s head slightly to the right.
Tum 1: Remove the hand and add blueberries evenly spread over the dough.
Turn 2:
Add pair of hands creating lattice pattern with daugh blueberries baking
strips top Remove the hands and complete the lattice pattern of blueberries
Tur 3: dough strips
Tum 1:
Change the visible part of the man's s face show
Turn 1:
Transform the simple fox like sketch into detailed female character with fox like features performing dance Change female character 's dance pose from having one arm raised and leg forward.
the eyes and forehead,
add hair on the forehead, and add red outlined white mark Pan down the comera focus on the man's 's nase and mouth outlined white patch from the forehead to the lower lip
forehead
lifted to having both arms extended and
and
the red
Turn 3:
Change the female character's to standing upright with arms raised add wings behind her
Tum 3:
Zoom out to show the full face the man, add hair, change the framing to include plain lackgruundd and change the expression neutrol
Figure 9: Examples (1/2) of visual transition annotation performed by our in-house large multimodal model.
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Stop the mon's 's hand gesturing and close mouth 1,
Tur 2:
Add curved stick to the man't hand and make him gesture with his night hand.
Tum 3:
Change the hand gesture from general gesture to rack hand gesture with the raised higher
Change the boy's action to running with
extended towards the basketball and the basketball to in front of boy on the ground, and change the boy's 's orientation to face more towards the left. Change the boy's action to standing upright and looking forward, move the basketball to the boy's right hand, and
Tum 2:
add a sun on the right side the sky.
Tum 3:
Add block X the boy shorts and change his holding the basketball to his face
and add white armchair with blonket, small black round table, floor lomp white shade, and potted plant.
Remove the
with hair, wearing yellow short sleeved shirt and block pants, stonding and facing away from camera with her right hand raised slightly to the room scene. change the
Add
action from walking and gesturing Tum 3: Add a woman with blande hair sitting on the armchair, holding a white cup and raising her hand.
stonding and touching the patterned curtain with both hands
Change the first woman's ss action standing and holding white cup and smiling. Add white cups, each woman's hand.
Tur 1:
Move the man closer to the SUV such that he is opening the rear door with his right hand, and change the SUV's rear open.
Tu
Edit the image to transition the man's pasition from standing outside the rear door of the SUV to being partially inside
the vehicle,
Remove the man getting into the SUV and close the rear door.
Tum the white SUV further down the and close right door.
Figure 10: Examples (2/2) of visual transition annotation performed by in-house large multimodal model.
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Remove the hands an the
Add two reaching towards the cups.
Interleaved Image-Text Sequence
cups slightly opart.
Interleaved Tokens
Inpust Ieaage
Tern Toleen
timger
condittinn
Rolted
Temi Talen
Confitien
(mage
Feat
Fest
Asput image
Zurr Tokan
Imsge
Contition
Zumm Toinn
Notted
lnsost traago
Tarn Tokaon
Arguur lmage
Kiors Tokee
Zont Nalirdd
Terr
Toxt
imrage
loport image
imrage Condition
Thrne Talen
Irguet Imagt
Toxt
Nowse tmsage
Tur Tokes Text
tmage Candation
Tuin Folin
Welsee Iragg
Thrns Takon
Contition
Hetted
(a) Block-wise Causal Attention
(b) Full Attention
Figure 11: Implementation of (a) block-wise causal attention and (b) full attention.
C.4 MODEL ARCHITECTURE
Variational Autoencoder. Following prior work (Yu al., 2023), adopt the encoder in pretrained VAE to embed each image into the latent space separately for efficient computation. Specifically, it compress with downsampling ratios d, A and dw W. for height and width, respectively, and the latent channel c. The decoder in VAE aims to transform latent representations generated by the DiT back into the pixel space during inference.
pixels with shape (H, 3,3) into (h, w, c)-shape latent representation,
Text Encoder. We employ the pretrained Flan-Tr T5 as the text encoder to separately encode the prompt in each turn, and then concatenate all the embedding with inserting turn embeddings in between. Specifically, to make the model better discriminate different turns, we define a special turn token <TURN>; for the i-th turn, and introduce a learnable turn embedding for each one, which is inserted before the prompt embedding in the i-th turn.
Full Attention and Block-wise Causal Attention. We show the comparison between full attention and blsc causal attention, and the condition strategy of clean context in block-wise causal attention, Fig. 11.
C.5 COMPOSITION OF INPUT CONDITIONS AND OUTPUT
In Fig. 12, enumerate all seven context compositions supported by method, detailing the interleaved input conditions, the corresponding outputs, the learning objectives, and the specific capabilities unlocked by each composition.
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Input Condition (InConntxxt
Output
Learning Objective
Ability
Source Image
Turn-1
Tum-2
Tum-3
Tur-4
Resave
Add he reaching...
the caps and
N/P
Editing
Rerrie the pradict
Add hwo for ii
Goumdiig
curnert
Renove
hards readeng.
FAST acgrentation
huncs Firett
NSP
Plarning
Layour
Remove
Add hvo reaciing...
hoecs Fert:
Heat
NSP
Add hwo
de
funds First: predich
Kanteat.
Grunnded
Editing
scgmentatin
titting
har readsing
predich the
ummtent
partom
NIP
Controlable Editing
Stgrestatias
Adl heo
Resove prodict the current
Second WesT
Boaed the perfamm editing.
Chain-of-E diting
readvvng
aagrantation
Figure 12: Context composition supported by method.
Tum Remove the person from the cheir.
Tum Remove the small orange flower buds from the cachepot design.
Tum 2: Add a cat sitting the chair.
Tum Replace the large red flower with
Tum Change the chair color
decerative butterffy ornament.
Change the flower color golden hue. Replace the cream background with a soft pastel gradiant.
Replace the background on urban park scenz.
Tum S: Change the image style resemble comic book art.
Tum Apply dolly-in comero effect to focus the butterfly
Tum Remove the vintage Volkswagen
Tum Add rainbow orching ocross the sky
Tum Chonge the highthouse
Tum Change kayak's appearance feature
and white striped design.
Tum 3: Replace the lighthouse with classic streetioms reetiomp.
duck sitting colmly
kayak,
Tum 4; Repiace the sky with starry night
Tum Replace racky seaside
fecturing crescent moon
garden landscape.
Tumn Add lify pad beside the knyak the river surface.
Tum Change the lighting to
dramatic moonlit setting.
Tum 1: AdAdd rainbew above the airplane.
Tum small, playful dog following the womon on the bike.
Tum 2:Change the airplane' orientation to vertical dive.
Tum 2: Medify the womon's is posture to wave her hand.
Tum 3: Add a fleck of colorful birds around the airplane.
Tum Reploce background
pork setting
Tum Change
background
featuring treas and pathway
Tum Pan the camera slightly the right to better center the womon en the bike.
Tum Depict the airplane performing
aerobatic loop with a visible smake trail.
Turn S: Chonge the man't is''s expression look amozed with open
Figure 13: Multi-turn image editing examples of MSE-Bench.
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C.6
DETAILS OF MSE-BENCE
Instruction for Evaluation of Multi-turn (turn-i) Image Editing on MSE-Bench
Assume you are an expert evaluating multi-tur turn image editing. this task, a user interacts with an image editing system across multiple turns. At the first turn, the user provides a source image and an editing prompt. The system returns the edited image. In each subsequent turn, the user supplies new prompt, and the system generates a image based on the output from the previous turn. Your goal is evaluate how successfully the editing instruction of the LAST turn has heen executed.
You will be given user editing prompts and images: the first image is the original source image, and the next are the edited results from each turn for each prompt. You should focus more on the last prompt and the last edited image, but you may also consider the previous prompis and images as context. The user editing prompts are: []
Please follow these evaluation rules: 1. Last-turn Evaluation: For the last turn, you should first assess the result based on two criteria by giving reason: 1) prompt_ followwng does the last edited image fulfill the last user's editing prompt? consistency: Are the untouched parts of the last result image consistent with the input reference (the source image at the first turn, or the result image the previous turm)?
2. Scoring: Based on the reason, you assign scores for "prompt_ following" and "consistency".
From scale 0 to 10:
A "prompt_followingg following" score from 0 10 will be given based the editing success of prompt following. (0 indicates that the scene in the last edited image does follow the last editing instruction all. 10 indicates that the scene in the last edited image follow the last editing instruction perfectly.)
A "consistency" score from 0 to 10 will rate the degrec of overediting in the last edited image. (0 indicates that the scene in the last edited image is completely different from the original. 10 indicates minimally edited yet effective version of the original.) Return your results a JSON structure, following this format: {(" {(reeson"" "...", ppromp__follownng int, "consistency": int)
that the last edited image can be recognized
The source images for our constructed multi-turn image editing benchmark, MSE--Bench, are sampled from MS-COCO (Lin et al., 2014) and randomly sample 6,000 images from each dataset and employ GPT-4o to perform prompt imagination, guided by criteria such as editing reasonability, aesthetics, consistency, and coherence. To facilitate this, we define set of editing operations (e.g., add, remove, replace) and design series of rules to instruct GPT-4o simulate realistic and coherent multi-ture editing prompts from real users' perspectives. The instruction used in this process is illustrated above. Following prompt generation, we conduct careful human filtering to remove low-quality cases, resulting final set of 100 high- quality, -baaanee- examples that constitute MSE-Bench. Additional examples are shown in Fig.13. 13.
(Schuhmann al., 2022). Specifically,
C.7 SUPERVISED FINE-TUNIN
After training on the constructed interleaved data from native videos, i.e., VINCIE-IOM, we carry out supervised fine-tuning to align the model with downstream editing tasks. Specifically, all of our SFT data comes from open-sourced datasets, including:
OmmiEdii-FilteredLLL. (Wei et al., 2024).
Web-Images and GRIT-Entity-New splits from X21-subject-driveni proposed in Omni- Gen (Xiao et al., 2024),
X212-video-editing 1212-inpaint-editing 222-in-context-generaaiie and X2I--in-context- editing splits from X212 proposed in OmniGen (Wu et al., 2025b),
SSED-Data-Edit-Part proposed by SEED-X (Ge et al., 2024).
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Table Human evaluation MSE-Bench based editing success rate. Entries by gray denote proprietary models.
indicates use of context.
Human Evaluation
Method
Turn-1
Turn-2
Turn-3
Turn-4
Turn-5
HQEdit (Hui et al., 2024)
0.170
0.073
0.020
0.003
0.000
UltraEdit (Zhao al., 2024)
0.310
0.062
0.015
0,002
0.000
OmniGen (Xiao et al., 2024)
0.333
0.035
0.002
0.000
0.000
GPT-40"
0.872
0.783
0.755
0.642
0.491
Ours*
0.661
0.500
0.323
0.209
0.070
D ADDITIONAL EXPERIMENTAL RESULTS
D.I HUMAN EVALUATION ON MULTI-TURN IMAGE EDITING
To further verify the effectiveness and superiority the proposed method for multi-turn image editing, we conduct human evaluations to assess editing success rates. The results are reported in Tab. These findings validate the benefits of training on native video data, combined with supervised fine-tuning pairwise editing examples, in enhancing multi-turn editing performance.
D.2 CORRELATION BETWEEN GPT-40 AND HUMAN EVALUATION
Table 7: Correlation between automatic metrics and human evaluation
Metric
GPT-4o vs Human
CLIP-T vs Human
CLIP-I vs Human
Pearson
0.4858 (p 0.0000)
0.0817 (p 0.4191)
-0.0549 0.5875)
Spcarman
0.4644 (p = 0.0000)
0.0692 (p = 0.4941)
-0.0217 (p = 0.8303)
Kendall T
0.4154 (p = 0.0000)
0.0502 (p = 0.4963)
-0.0195 = 0.7921)
In our experiments (Sec.4), primarily report GPT-4o evaluated success rates assess multi-turn image editing performance. To validate the reliability GPT-4o-based evaluation, we compute the correlation between GPT-4o scores and human judgments. As shown in Tab.7, also compare other metrics such as CLIP-T and CLIP-I. The results demonstrate that GPT-4o correlates well with human evaluation, supporting its use a reliable proxy for scoring multi-turn image editing.
D.3 HUMAN EVALUATION FOR VLM ANNOTATION
To further verify the validity of the proposed automatic interleaved data construction pipeline, we conducted a human evaluation for VLM annotation accuracy and recall, shown in Tab. 8. While current VLMs are imperfect, they offer scalable data annotation solution, achieving a favorable balance between quality and scala- bility. Similar to prior works (Brooks et al., 2023), our work aims the constructed large-scale in-
Table 8: Human evaluation for VLM annotation 500 data instances randomly sampled from our training dataset.
to explore continual pre-training terleaved corpus, where data scale is critical and minor annotation noise is tolerable. Besides, we believe the proposed automatic data construction pipeline will become increasingly effective, with the rapid advancement of large multimodal models.
Metric
Score
Accuracy Recall
75.14%
69.06%
D.4 ADDITIONAL ABLATION STUDY
Impact of RoPE and Attention. Based on video foundation model, VINCIE continues pre-training on the constructed interleaved text-image data. In the foundation model, we first concatenate text tokens and video tokens into sequence. perform Rotary Position Embedding (RoPE) (Su et al., 2024) on it, and then feed it to multiple MM-DiTT (Esser et al., 2024) layers for video modeling. Full bidirectional attention is adopted in each layer for thorough moral and cross-modal interaction.
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Table 9: Impact of segmentation (seg.) prediction and camera prompt engineering (PE), i.e. inserting "[#H#AMEEA: None#H#]]" before each user prompt, on consistency evaluated by CLIP-I and DINO scores on Magicbrush (Zhang et al., 2023a).
CLIP-I Score
DINO Score
Train
Inference
Tum-1
Turn-2
Turn-3
Turn-1
Turn-2
Turn-3
w/o Seg.
0,875
0.824
0.784
0.765
0.663
0.592
w/ Seg.
0.880
0.832
0.797
0.786
0.680
0.604
w/ Seg.
Camera PE
0.884
0.832
0.798
0.798
0.681
0.612
Table 10: Ablation study on MSE-Bench (GPT-4o evaluated success rate), to assess the impact of RoPE and Attention.
RoPE text-then-image interleaved interleaved
Attention
Turn-1
Turn-2
Turn-3
Turn-4
Turn-5
full full block-causal
0.968
0.360
0.320
0.238
0.160
0.933
0.338
0.308
0.245
0.183
0.880
0.290
0.230
0.200
0.120
To explore the impact of RoPE and Attention, we design three variants and conduct performance comparison on MSE-Bench, as shown in Tab. 10. Considering the foundation model has carried out large-scale pre-training text-then-image RoPE and full attention. This strategy achieves the best performance on Turn-I 1 to Tumn33. However, the interleaved RoPE gradually outperforms it the sequence length increases. One reason is that the interleaved RoPE arranges the text and image more naturally than the trivial text-then-image iinage strategy. Finally, block-causal attention performs the worst, which may be attributed to the limited modality interaction. However, block-causal attention shows strong potential, offering flexibility in next-block modeling, support for prefill decoding to enable efficient inference, and compatibility with LLMs, which we leave for future work.
text-video data, it has attained strong prior knowledge based on
Impact of Camera Motion in Training Video Data. highly required. To delve into possible entanglement issues of camera and object movement, we have adopted a disentanglement learning strategy consisting of: 1) Explicit annotation of camera change (see the instruction in Sec. C.2); 2) Incorporation of camera prompt wrapped in special tokens, such as "[##CAMERA: pan-lefu##*)" during training: 3) Use of static camera prompt, L.e., "[##CAAMEA: None###J", during inference. This strategy enables the model to disentangle camera movement from object dynamics, allowing flexible camera control based on application needs. The results shown in Tab. 9 verify its effectiveness in improving consistency.
In most editing scenarios, consistency is
D.5 ADDITIONAL PERFORMANCE COMPARISON ON STORY KEYFRAME GENERATION
Tab. Il provides a quantitative comparison of story keyframe generation performance between our method and the recent method, i.e., In-context LoRA Huang et (2024) on the benchmark introduced in LCT (Guo et al., 2025), serving as empirical evidence of the effectiveness of our approach. In this work, we aim introduce general video-driven learning framework unlock in-context image editing and generation, with story keyframe generation being one potential application. Due to the limited time, we focus on multi-turn image editing, while more comprehensive evaluation of other capabilities, including story generation, is left for future work.
Table 11: Our method vs. In-context LoRA (Huang et al., 2024) with human evaluation on the benchmark introduced in LCT (Guo et al., 2025) for story keyframe generation. Evaluation is carried out from two aspects: prompt following and consistency.
Metric
Win
Fail
Tie
Prompt Following
55.10%
16.30%
28.60%
Consistency
43.80%
2.10%
54.20%
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Replace the wooden table with white marble surface and add another small red decoration.
Change blender's exterior to metallic finish.
Add rainbow, and blue sky and cloudy days interweave.
Change the grass clay court.
It appears to be snowing.
Generate monochrome- style animation.
Transform this image into Pointillist artwork.
Transform into oil painting style.
Figure 14: Zero-shot qualitative results of single-turn image editing on cases uncommonly present in video data. The model was only trained with interleaved session data from video, T2I data, and T2V data.
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E
ADDITIONAL APPLICATION EXAMPLES
IMAGE EDITING
Source Image
Turn Remove the person from the chair.
Turn 2: Add cat sitting the chair.
Tur 3: Change the chair color to vibrant pink.
Turn 4: Replace the background with an urban park scene.
Turn 5: Change the image style to resemble comic book art.
Tum 1
Tum 2
Tum
Tum
Tumn 5
Bagel*
FLUX.1 Lix.1-Kontext(or (dee)) (dev)
Qwen-Image-Edit
GPT-40
Nano Banana*
Ours* (7B) +SFT
Figure 15: Qualitative comparison (1/4) between our method and recent baselines on MSF-Bench.
Single-turn Image Editing. In addition to common scene changes present in video data, we observe that our model generalizes well uncommon cases, such as abrupt environmental shifts, complex style transfers, and material transformations (Fig. 14). This capability may arise for two reasons. First, although infrequent, such patterns (e.g., environmental changes) are still present in our training corpus. Second, the model is initialized from video foundation model that has been extensively pre-trained on both T2I and T2V data, enabling it to internalize high-level concepts such as style and material. These derived capabilities can be naturally transferred to the image editing setting.
Multi-turn Image Editing. As shown in Fig. 15, we compare our method with several baselines, including HQ-Edit (Hui et al., 2024), UttraEdit (Zhao al., 2024), OmniGen (Xiao al., 2024),
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Source Image
Tum 1: Add rainbow arching across the sky.
Tur 2: Change the kayak's s appearance to feature intricate patterns.
Tum 3: Add duck sitting calmly on the kayak.
Turn 4: Replace the sky with starry night featuring a crescent moon.
Tum 5: Add lily pad beside the kayak on the river surface.
Tur
Tur 2
Tum
Tum
Baget+
Qwer-Inage-EGd Wox.teKontext (dev)
GPT-40
Nano Banana*
Ours (7B) +SFT
Figure 16: Qualitative comparison (2/4) between method and recent baselines MSE-Bench.
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Source Image
Turn 1: Add small puppy playing near the girl.
Turn 2: Change the color of the girl's ss coat to teal.
Turn 3: Replace the snowy background with spring meadow.
Turn 4: Move the fire hydrant slightly to the right.
Turn 5: Change the girl's posture to crouching as if she is petting the puppy.
Turn 1
Tum 2
Tum 3
Turn
Tum
Bagel*
FLUX.1-Kontex (dev)
Qwen-Image Ediit
GPT-4o*
Nano Banana*
(7B) +ST
Ours* (7B)*(7B)
Figure 17: Qualitative comparison (3/4) between our method and recent baselines on MSE-Bench.
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Source Image
Tum 1: Remove the from the left side of the image.
Tum 2:
Change the background a highland mountain landscape.
Tum 3: Alter the Defender's 's paint to metallic silver color.
Tum 4: Replace the Defender with vintage convertible car.
Tum 5: Apply cinematic color filter to the image.
Tum 1
Turn
Tum
Tum
Bagel*
FLUX. FLUX.1 Montext (dev)
GPT-40 Owen-Image -aage-Edlt Edit
Nano Banana
Ours* (7B) +SFT
Figure 18: Qualitative comparison (4/4) between our method and recent baselines MSE-Bench.
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and GPT-4o. The results reveal several key observations: 1) Most existing models suffer from error accumulation, leading to imcreasingly severe artifacts across editing turns. 2) These accumulated errors often degrade prompt-following performance, where the model fails to execute edits instructed once artifacts dominate. 3) While GPT-4o-a strong proprietary model-achieved competitive results, may exhibit inconsistencies in cases compared to our method. 4) Overall, these comparisons highlight the effectiveness of training on native video data for achieving coherent and prompt-aligned multi-turn image editing. Additional qualitative examples provided in Fig. 26, Fig, 27, Fig. 28, and Fig. 29, further demonstrating the strong prompt-follwwing and consistency of our approach across multiple editing turns.
E.2 MULTI-CONCEPT COMPOSITION
sneaker and toy cobblestone street
bowl and can on top of white rug
dog and dog with blue house in the background
a vase and stuffed animal on the ground
shiny cat, next to it backpack
a dog can on the ground
dog wearing rainbow scarf, next to is dog and toy with mountain in the background
on the sofa
the patterned ground
robot toy and dog
in the snow
in the brick background
in indoor scene
a monster toy, a white dog, brown dog, and a sneaker
Figure 19;
Zero-shot qualitative results of multi-concept composition (in-context generation) achieved by our method (without any fine-tuning).
In-context Image Generation. In Fig. 19, we present qualitative results on in-context image generation for multi-concept composition, which requires both composition and strong identity
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Replace the women <IMG2> with man <IM61>
bowl <IMG1> front the toy bear held by the old woman in <IMG2>
M
Apply the background from <IM& -IMG2>.
the background <IMG2> with the red brick background from <IMG1>.
Make the girl <IMG2> have the same expression with the woman <IM61>.
Recolor the helmet in <IM62> using the color of the sweater from <IMG1.
Figure 20: Qualitative results of multi-concept composition (in-context editing) achieved by method. Our model is fine-tuned on the X212 (Wu et al., 2025b) dataset, but the shown transferred concepts (e.g., background, color, and expression) uncommon in X212. It demonstrates the strong generalization ability conferred by pre-training on video-based interleaved data.
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preservation. These examples demonstrate that only training video data (without any fine-tuning) can effectively unlock compositional capabilities, despite the rarity of such patterns in typical video content. This emergent behavior highlights the potential of video-based pre-traine tre-training. Further scaling of model capacity, compute resources, and video data may enable the emergence of even more advanced capabiliries. For instance, enhanced identity preservation enables more effective personalization (Wang et al., 2025; Zhao et al., 2025; 2026).
In-context Image Editing. In addition, we conducted further supervised fine-tuning on the X212 (Wu et al., 2025b) dataset to explore more advanced multi-concept composition abilities, The qualita- tive results (Fig. 20) on in-context image editing indicate that even lightweight SFT substantially incentivizes more powerful compositional editing abilities, highlighting the effectiveness of our video- driven pre-training. Notably, compared with Fig. 19, our fine-tuned model generalizes beyond object-centric concepts, such as background, color, and expression, despite these concepts being uncommon in the fine-tuning dataset (Wu et al., 2025b).
E.3 STORY GENERATION
Choracter womon with long brown hair, wearing light-colored top.
Tum
Transform
from large robot crashing amidst debris with people scottering focused battle
city street.
Tum
Trensition from a dynomic bottle scene involving robots domoged urbon setting to close-up womon opersting vehicle's controls, emphasizing personal struggle over large- scale action
Tur 3:
Make the women' expresse ession more desperate. Add a thin metallic object to her hand to indicate urgancy in her actions. Transition from interior vehicle scene with Character trying stort the cor, to outdoor chootic scene fecturing giont robot, soldiers, from smoky. cheotic city street scene
Tum 4:
possing black
Tur
towering robot watching soldiers close up, detailed view context and ambient elements.
with glowing eyes, eliminating widen the focus from the robot's s face to include a scene of combat with intense explosions and debris, adding context and act ion to the static oppacrance of the robot frame 1. Decresse the flomes ond smoke expose: the robots and enhonce their detoils the frome.
Turn 6:
Tum 7:
Tum
Tumm
Tur
Turn 5
Turn 6
Tum 7
Character man with curly hair wearing patterned blue shirt. Character 2: mon with short, slightly curly hair, mustoche, and gostes, wearing blue shirt. Character
long gray
cowboy hat.
Tum 1:
Shift focus from Chonscter Cheracter holding the wine gloss his nose, with Choracter slightly repositioned d to the background. held the glass similar second frame, while maintaining
Tum 2:
Add Character to the second frame, adjusting Choracter 2's lighting.
Tum Widen the shot include third character with long gray
a cowboy behind the counter, and counter to tronsform the scene from a close-up up to wide shot, encompassing group tasting session.
wine bot ties
Tum 4: Change from a wide shot in a wine tosting room to medium close-up shot focusing on Character i and Character with blurred
background, removing Cherocter and visible wine bottles.
Tum 5:
Shift the from characters' upper body Frome: their hands swirling glosses Frome emphosizing interaction
Figure 21: More qualitative results of story generation achieved by our method.
Since our method is trained on native video data, inherently captures the underlying storylines present in the sequences. As illustrated in Fig.21, we formulate story generation as multi-turn image editing task, guided by transition prompts between key frames during inference. These examples showcase the model's ability to follow prompts while maintaining coherence and consistency across turns. When combined with existing long video generation methods(Guo et al., 2025), our approach has the potential enhance top-down planning for generating coherent long-form story videos.
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Add effervescent bubbles to the bigger champagne glass
Add rabbit beside the leftmost broccoli.
current seg.
seg
Add a butterfly at the top of the tree.
Add people colorful clothes the road
sog.
seg.
Add golden retriever the Mustang's front bumper.
Ratate the car to angle its front end more prominently
next seg.
seg.
Add small, playful dog following woman on boy perform jump.
next
next seg.
Make the black and white dog stretch its legs and yawn.
Add small bird perched the head of the bigger bear.
current seg.
next
current seg.
next seg.
Change the action of the skateboarder to performing a mid-air
Make the man and woman hold hands under the umbrella.
current seg.
next seg.
seg.
next seg.
Figure 22: More qualitative results of Chain-of-Editing
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Source Image
w/o CoE
w/ CoE
Rotate the bass guitar to create a diagonal orientation
current seg.
Adjust the puppy's posture to a lying down position with its head raised.
current
seg.
Add small bird perched on the cow't head,
current seg.
seg.
Animate the yoga pig cookies to show movement between poses
current seg.
next seg.
Change the posture of the left Minion to show it dancing
current seg.
next
Add choir of children singing around the altar
seg.
seg.
Adjust the posture of the cowboy ornament to mimic hat-tipping gesture.
current seg.
next seg.
Figure 23: Qualitative comparison between w/o Chain-of- (CoE) and w/ CoE.
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E.4 OF-IIING
In Tab. 3, we show the effectiveness of chain-of-editing, i.e., predicting segmentation maps before performing image editing. The predicted segmentation maps could be viewed kind of "thoughts" In Fig. 22, we show more qualitative results for challenging cases to demonstrate the effectiveness of CoE.
E.5 DRAG-BASED IMAGE EDITING
Current Seg. Prediction
Drag-driven Seg. Transformation
Controliable Editing
Modify the car
Figure 24: Qualitative results of drag-based image editing.
Source Image
Move this upwards by 1cm.
Move this upwards by 10cm.
Move this can upwards by 1m.
Nano Banana
GPT Image
Ours (darg-based control)
Figure 25: Qualitative comparison for subtle displacement editing.
The current and next segmentation prediction tasks introduced in Sec. 3.3 not only support progressive planning and generation, but also enable controllable editing for enhanced user interaction. One such
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application is drag- drag-based image editing for object displacement, scaling, and rotation, illustrated in Fig.24. In this setting, users first provide an editing prompt to localize the RoE. Then, drag operations applied to perform geometric transformations of the RoE. The transformed segmentation map driven by the transformation incorporated into the context, allowing the model to generate a target image that adheres the specified edits.
Despite the strong understanding capabilities (Liu et al., 2018; Qu et al., 2024a) of VLMs, they may still struggle detect subtle semantic visual differences when frames differ only minimally, As illustrated in Fig. 25, we first present qualitative results from the most advanced proprietary systems GPT Image (OpenAI, 2025a) and Nano Banana (DeepMind & Gemini, 2025)-on on the task of subtle displacement editing. We then showcase our drag- based editing results, demonstrating that this challenging requirement can be effectively addressed through the more flexible and fine- grained control (i.e. drag). This comparison highlights the versatility of our method.
F LIMITATIONS
Discussion of Other Potential Limitations. First, we use T5 to encode text, which restricts the model's ability to comprehend complex instructions and generate nuanced textual outputs. Integrating vision-language model (VLM) into the framework could significantly improve this capability. Second, while our framework demonstrates preliminary but promising emerging abilities, these can be further enhanced through supervised fine-tuning (SFT) high-quality, application-speciin datasets. Lastly, due to the high cost of querying VLM, we annotated only 10M training samples. Expanding both the model size and the dataset scale presents exciting avenue for future research.
G FUTURE WORK
In the future, aim solve challenging image creation tasks (Qu et al., 2023; Yang al., 2024a; Qu et al., 2024b) with complex and compositiona prompts, by exploring multimodal chain- of-thought. Besides, post-training (Qu et al., 2025b; Zhou et al., 2025c; Gong et al., 2025) would stimulate more potential interesting abilities endowed by learning from videos. Finally, by introducing retrieved images (Qu al., 2025a; Chen al., 2022; Qu et al., 2021; Wen et al., 2024) into context, our model could achieve noowledgeinntensing visual creation scenarios via retrieval- generation.
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Tum 1: Add climbing vines with around the bicycle and window.
Turn 2: Apply warm golden sunlight effect across the scene.
Turn 3: Replace the reflection the window with a garden scene.
Add balloons colorful child hand.
Replace the bockground snowy mountains.
small child,
the brightness sunny winter effect.
Turn 1: Add a fluffy white cot curled up on the bed.
Turn 2: Replace the florol duvet with a vibrant patchwork quilt.
Turn 3: Replace the wall with a mural of serene countryside scene.
Turn 4: Adjust the lighting to be söfter and warmer.
Turn green potted plant table lomp.
Turn Replace the two laptop screens landscape images.
Turn change black bottle tronslucent gloss
Apply colorful glow light lamp.
from desk
Turn flowers violet and blue shodes.
Add ivy creeping clong the stone
the
butterflles the flowers.
Figure 26: More qualitative results (1/4) of multi-turn image editing achieved by our method.
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colorful a flying above the scenk.
gently touching ginefie's a snout.
ginoffe's head Blightly towards the woman.
background with African zavanna londscope, sunset.
upwards slightly to enhoncce the giraf fe height egarist sunset.
Tum Add bouqust of colorful flowers the table.
Turn 2: Reploce the writing the whiteboard with
Tur S: Change the lighting create wormer and more inviting
Turn Change the posture of the nearest porson excitedly outside.
Tur & Rep the outside view with beautiful garden
Tumn Remove the person
Tum Add a oot sitting
Tum Change the cheir
Tum Replace, the bockground with
Turn Change the image style book
Turn Add glosses Tediy bear:
Turn Add a lottien playing with book
Tur Charge the background mystical forest:
Turn Add mogical glow scane.
Turn Add wand fediyy pow.
Turn 1: Remove field.
Turn 2: Add vintage field
Turn Enhonce the rainbow make them more vivid.
Turn Replace clesner one featuring bright surlight and curfules clouds
Turn Apply a with brighter colors and worm lighting.
Apply cinematic color filter to the image.
free from left side of the image.
bockground highlond mountain landscape.
Defender's metallic silver color.
Defender vintage convertible car.
Figure 27: More qualitative results (2/4) of multi-turn image editing achieved by our method.
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1: friendly sitting beaide the girl.
Tum Change girl's to vibrant reinbow pottern.
Tum Replace background with whimstcal pork scene featuring blooming treed Blue sky.
Tum Change her oppeor she is twirling.
Apply soft pastel filter the entine image.
Tum 1: Add colorful ort munci on the building wall.
Turn 2: Reploce the blue truck with vintage red pickup truck
Tur Charge the red deor to Be open, revealing cozy interior:
Tum d: Change the scene to righttime with ambient lighting.
Turn Add black and white cat sitting on top of track.
Tum 1: Replace the bockgrounee ound with on uroon skyline during day.
Tur Change the shirt color to deep maroon.
Tur 3: Add friendly robot stonding besice the
Tum di Dolly focus on the man and the robot.
Tum Adjust the lightting to create cool and diffuse atmosphere.
Change zomble pesture icon forward as if accelerating.
Replace zombie with a futuristic helmet design.
stylish decals scooter.
Introduce citysoope background with street lights and subrie ban elements.
groffin elements to the background.
Turn 1: Replace the foground strest with graffiti.
Tur 2: Add group of cartoenish the hot admiring
Turn 3: Change the rod
Turn 4: Apply a dally-in in Turn Add comera anglé cortoon dog playfully emphosize red.
the
Figure 28: More qualitative results (3/4) of multi-turn image editing achieved by our method.
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colorful crochet hat.
Slightly the comeni
of colorful blossoms behind the dog.
exprestion be more playful and happy.
beside the
Tum Chonge swaying gantly to the music,
Replace stylized Parision street scene.
Adjust show it joyfully engeged with the music.
animated the show interaction with the music.
the cow shoulder.
Turn Chonge the oor model color
Turn Add a cleasic suitoosl
Tum Replace the background urban streat scane.
Tum Replace the hubcaps wire- spoke wheels
Tum Add motion indioate the driving.
Tur Remove the cups glass from the fray.
Tum Add a coctus plant the tray.
Tum 3: change the blue.
Turn 4: Replace the backdrop.
Turn 5: Add vibrant butterfly on cactus.
Turn 1: Replace the teddy bear with plush giraffc wecning
Tum 2: Reploce the greanery with colorful garden of plants.
Turn 3: Change the gin''s dress to feature reinbow patteerss
Turn 4: Add plush panda sitting between the girl gireffe.
Tur 5: Tronsform the tea set Into magical whimsical set.
Figure 29: More qualitative results (4/4) of multi-turn image editing achieved by our method.
46


Reviews:
[{"review_id": "NskZ7ze7SK", "reviewer": "Reviewer_wj9T", "summary": "This paper introduces VINCIE, a framework for in-context image editing using video data. The core premise is to learn this capability solely from native video data, bypassing the need for curated, paired image-editing datasets. The authors propose a scalable data pipeline that samples video frames and uses a Vision-Language Model to generate textual annotations of the visual transitions between them. It also generates segmentation masks for these \"Regions of Editing\" (RoEs). The model, a diffusion transformer, is trained using three proxy tasks: Next-Image Prediction (NIP), Current Segmentation Prediction (CSP), and Next-Segmentation Prediction (NSP). To evaluate their method, the authors introduce a new multi-turn benchmark, MSE-Bench, which consists of 5-turn editing sessions and is evaluated using GPT-4o. The results demonstrate that VINCIE achieves strong performance, outperforming many academic methods on MagicBrush and their new benchmark, and that the approach scales well with data.", "questions": "Natural videos excel at capturing state transitions like object addition/removal, pose changes, and motion. However, they rarely, contain examples of more \"creative\" or \"unrealistic\" edits that are common user requests. For example, drastic environmental changes (e.g., \"change the weather from snow to summer\"), complex style transfers (e.g., \"make this look like a Picasso painting\"), or material transformations (e.g., \"make the person out of glass\"). It is questionable whether a model trained only on natural video can generalize to these common editing tasks. I do agree that video data could be used in the pretraining, but using video data for image editing is not a novel concept. The paper's own related work section cites several recent methods that also \"Learn from Video for Image Generation.\" The paper differentiates itself by using \"long, interleaved image-text context\" rather than just frame pairs, but this feels more like a strong incremental improvement than a paradigm shift.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The central idea of using native video data to learn in-context editing makes sense. It cleverly reframes the problem, identifying videos as a natural, abundant, and scalable source of \"edit\" data (i.e., state transitions) that intrinsically contains the visual consistency and temporal context missing from static image-pair datasets. \n\n2. The data construction pipeline is sound, combining a VLM for high-level semantic transition annotation with grounding models (GroundingDINO + SAM2) for precise, low-level spatial localization (RoEs). The three proxy tasks (NIP, CSP, NSP) are well-motivated; using segmentation prediction not only as a training signal (CSP) but also as a predictive target (NSP) is a smart way to improve grounding and controllable generation.\n\n3. The paper is well-written, and the methodology is presented clearly. The figures are effective at illustrating the data pipeline and model architecture.", "weaknesses": "1. The paper defines in-context editing as modifying an image based on a \"contextual sequence comprising text and previously generated images.\" This framing is functionally equivalent to multi-turn editing, where the primary role of the context is to serve as the input for the next step. This definition is quite narrow and overlooks a more common interpretation of \"in-context\" for image models: the ability to use one or more reference images in the context to provide new subjects, styles, or concepts for the current edit (e.g., \"add the dog from the second image with the style from the 4th image into this one\"). The paper's experiments all fit the multi-turn definition, and it's unclear if VINCIE can perform this more complex, composition-focused \"reference-based\" in-context editing.\n\n2. The evaluation, while thorough on MagicBrush and the proposed MSE-Bench, feels incomplete. It would be much more convincing to see a comparison on more recent benchmarks, such as GEdit-Bench and also evaluate on the EMUEdit benchmark? \n\n3. The primary evaluation for the newly proposed MSE-Bench relies on a \"success rate\" computed by GPT-4o. This is a significant concern. The reliability of current VLMs for nuanced, multi-step comparative judgment is questionable. The appendix suggests multiple images are fed simultaneously, which could easily confuse the model. My own experience suggests GPT-4o can struggle to correctly identify subtle edits or preserve consistency even in simple source-vs-target comparisons, let alone a complex 5-turn sequence where errors propagate. This evaluation method needs much stronger validation.\nAlso, for automatic evaluation, it would be beneficial to also report scores like the PF (Prompt following) and SC (semantic consistency) from recent work like VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation. These are designed for image editing and might offer a more reliable supplement to the GPT-4o-based success rate.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ezp2C3AhC2", "reviewer": "Reviewer_bA7V", "summary": "This paper proposes VINCIE, a method for learning in-context image editing directly from video data. The key innovation is constructing training data by: (1) sampling frames from videos, (2) using VLMs to annotate visual transitions between frames, (3) generating segmentation masks for regions of interest using GroundingDINO and SAM2, and (4) training a diffusion transformer with three proxy tasks (next-image prediction, current segmentation prediction, next segmentation prediction). The authors introduce MSE-Bench, a challenging 5-turn editing benchmark, and demonstrate competitive performance against existing methods while showing scalability with increasing data.", "questions": "see the Weaknesses.  Overall, I would not object to this paper being accepted.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Well-structured with clear figures (Figures 1-3 effectively convey main ideas)\n2. Comprehensive appendix with implementation details\n3. Three proxy tasks (NIP, CSP, NSP) provide complementary learning signals\n4. Comprehensive ablation studies validate design decisions", "weaknesses": "1. The claim of learning from \"native videos\" is misleading—the method requires extensive preprocessing with VLMs, GroundingDINO, and SAM2. This is annotation-based learning, not purely native video learning\n2. Evaluation relies heavily on GPT-4o as judge, which may introduce bias despite correlation analysis (Table 7)\n3. Core technical novelty is limited: DiT architecture, segmentation prediction, and in-context learning are established techniques. The main contribution is the data construction pipeline\n4. Heavy reliance on existing models (VLM, GroundingDINO, SAM2) reduces the \"learning from video\" novelty\n5. Fair comparison at same dataset?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aez0XNjRda", "reviewer": "Reviewer_yqUb", "summary": "VINCIE introduces a novel approach to multi-turn image editing by directly leveraging video data. It treats sequential video frames and their natural transitions as in-context editing demonstrations, and designed three proxy tasks: next-image predic-\n047 tion, current segmentation prediction, and next-segmentation prediction to tickle this problem. Using a diffusion transformer architecture guided by predicted segmentation masks, VINCIE learns to localize and apply edits step-by-step in a context-aware manner without needing manually curated editing datasets. The method shows strong performance on new and established multi-turn editing benchmarks.", "questions": "Q1) In Visual Transition Annotation, authors leveraged VLM for captioning. While it makes sense to me to use VLM for generating detailed and coherent descriptions of each frame from multiple aspects, I am concerned of using VLMs to identify semantic and visual differences\nbetween the two frames when the two frames have minor differences. It is known that VLMs often fails in telling subtle difference between two images, as pointed out in The Dawn of LMMs and VIEScore. I question the robustness of this approach for fine-grained visual transition annotation, as critical details might be overlooked or misrepresented when using these models. Could the authors provide a case study or qualitative results focusing on more fine-grained edits (e.g. moving an object slightly to one direction, like only 1cm)?\n\nQ2) Regarding Regions-of-Editing, GroundingDINO and SAM2 may propagate errors in mask generation or transition description, especially in complex or cluttered scenes. Any measures are conducted to minimize the error impact in this pipeline?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1) This paper proposed a new perspective in constructing session-wise data with long, interleaved image-text context from native videos while prior works that used video for editing were mainly for constructing pair-wise data. This highlights the originality.\n\nS2) At first glance the designed approach is best suited for videos with a static viewpoint. When the camera moves, the region proposal and segmentation process can break down, leading to inaccurate edits or mask predictions. Large background or subject position shifts caused by camera movement are not explicitly addressed. But it is clever approach that predicting segmentation masks before editing could mitigates subject drift.\n\nS3) The paper is well written, with good motivation, and communicated clearly. Overall I enjoyed reading this work.", "weaknesses": "W1) I find it surprising that the paper identifies MagicBrush (NeurIPS 2023) as the last major benchmark for multi-turn image editing, with no subsequent progress. Based on my recollection, ImgEdit-Bench (May 2025) also introduced evaluation protocols on multi-turn image editing. Including results on the ImgEdit-Bench benchmark would make the empirical comparison much more convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Df6rn4S77k", "reviewer": "Reviewer_6Gue", "summary": "The paper proposes VINCIE, a video-driven approach to in-context image editing that learns multi-turn image editing directly from videos rather than paired images. In the method, videos are converted into interleaved multimodal sequences of sparsely sampled frames, VLM-written transition texts, and segmentation masks for regions of editing. The method leverages a DiT with block-wise causal attention trained on three proxy tasks: next-image prediction, current-segmentation prediction, and next-segmentation prediction. The authors also introduce MSE-Bench, a 5-turn editing benchmark, and report strong results on MSE-Bench and MagicBrush, with additional analyses on context, segmentation prediction, and data scaling.", "questions": "See weaknesses above", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The proposed pipeline of training multi-turn image editing models from video data is novel and intuitively effective.\n2. The proposed MSE-Bench can benefit the image editing community and more properly evaluate multi-turn image editing models.\n3. VINCIE achieved strong multi-turn image editing performance compared to previous art.", "weaknesses": "1. What are the training objectives for the segmentation prediction tasks NSP and CSP? Are they MSE or some other losses between the predicted and GT segmentation masks?\n2. Can the author provide some intuition on why adding the next segmentation prediction task during inference benefits MagicBruch scores but damages MSE-Bench scores (compared to CS->I)?\n3. From Table 4, it seems that the Dummy context is more effective than providing the editing history to the model. Is this the correct interpretation of this table? Does that mean the Dummy context itself is enough for multi-turn image editing?\n4. Based on Section 4.4, the camera movement and subject position shift problems can be solved by incorporating the segmentation prediction task during inference. Can the author elaborate more on why this prediction task helps stabilize the movement of the subjects in the image? Is it because the predicted segmentation mask always tends to be the same mask as in the previous editing round?\n5. The paper mentioned both full attention and block-wise causal attention in the method section. Which one is the method that is used in the final version of VINCIE? From Section 1, it seems that block-wise causal attention is actually used in VINCIE. If this is the case, what is the point of mentioning the full attention design? Or does the model support both attention mechanisms (across different editing turns) natively?\n6. I would suggest adding some description on how the model is used during inference, especially on the different modes that the model can be used, since the model accepts a wide variety of input conditions and these inputs can be quite flexible. Right now it is a bit confusing on how the model operates during inference: for example, when adding the segmentation mask prediction task during inference, will the predicted segmentation mask be used as part of the input condition to the model, or will it only be predicted, but not used during the next edited image prediction?\n\nOverall, I find the method and results to be promising, but I believe the current paper's presentation can be improved. I would be happy to raise my score if the concerns can be addressed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)