Unified In-Context Video Editing
Authors: Zixuan Ye
Xuanhua He
Quande Liu
Qiulin Wang
Xintao Wang
Pengfei Wan
Di ZHANG
Kun Gai
Qifeng Chen
Wenhan Luo
Venue: ICLR 2026 Poster (2026)
Keywords: video editing; video generation; diffusion models
Primary area: generative models
TL;DR: a parameter-efficient and unified framework for video editing tasks

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

Published as a conference paper at ICLR 2026
UNIFIED IN-CONTEXT VIDEO EDITING
Zixuan Ye Xuanhua He
Quande Liu
Qiulin Wang"
Xintao Wang²
Pengfei Wan Di Zhang²
Kun Gai? Qifeng Chen
Wenhan Luo
'The Hong Kong University of Science and Technology
Kuaishou Technology
https: yuaanny...ithhle io/UNIC
ABSTRACT
Recent advances in text-to-video generation have sparked interest in generative video editing tasks. Previous methods often rely on task-specific architectures (e.g., additional adapter modules) or dedicated customizations (e.g., DDIM inversion), which limit the integration of versatile editing conditions and the unification of various editing tasks. In this paper, we introduce UNified In-Context Video Editing (UNIC), simple yet effective framework that unifies diverse video editing tasks within a single model in an in-context manner. To achieve this unification, we represent the inputs of various video editing tasks three types of tokens: the source video tokens, the noisy video latent, and the multi-moda conditioning tokens that vary according to the specific editing task. Based on this formulation, insight is to integrate these three types into a single consecutive token sequence and jointly model them using the native attention operations of DiT, thereby eliminating the need for task-specific adapter designs. Nevertheless, direct task unification under this framework is challenging, leading to severe token collisions and task confusion due to the varying video lengths and diverse condition modalities across tasks. To address these, we introduce task-aware RoPE to facilitate consistent temporal positional encoding, and condition bias that enables the model to clearly differentiate different editing tasks. This allows our approach to adaptively perform different video editing tasks by referring the source video and varying condition tokens "in context", and support flexible task composition. To validate our method, construct unified video editing benchmark containing six representative video editing tasks. Results demonstrate that our unified approach achieves comparable performance with task specialists and exhibits emergent task composition abilities.
key
INTRODUCTION
Recent years have witnessed significant advancements in text-to-video foundation models based on diffusion (Chen et al. 2023 Wang et al. 2025a: Hong et al. 2022 Yang et al. 2024b] HaCohen al. 2024 kli 2025). establishing powerful tools for creating video content. Beyond the generation, video editing emerges as a natural extension, which aims to re-generate a reference vidco under multi-modal conditions, often incorporating fine-grained control signals like subject ID (He et al.) 2024; Zhang et al. 2025 Yuan et al. 2024) and artistic style (Liu et al. (023) along with textual prompts. Video editing spans diverse tasks, including global editing like style transfer (Liu et al.. 2023, Ye et al.] (024), video propagation (Liu et al.) 2024a Ku et al. (0244) and local editing like object insertion, removal, swap (Tu et al.] 2025 Zi et al. 2025 Lee et al. (025). as well as video re-rendering like re-camera control (Bai et al. 2025a). These tasks hold vast potential for applications in film production, virtual reality, and automated content creation.
Current video editing methods primarily follow two strategies to inject reference video and control signals. As depicted in Fig. 2 one stream of methods, represented by Video-P2P (Liu et al. 2024b), Any (Ku et al. 2024), and FLATTEN (Cong al. 2023), utilizes DDIM inversion for noise initialization to preserve the main structure of the reference video. However, these methods often fail to achieve ideal results and will inevitably introduce an additional stage, doubling the inference
Equal contribution. Work done during an internship at KwaiVGI, Kuaishou Technology.
Corresponning author.

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Published as a conference paper at ICLR 2026
Insertion
landecapo.
Stylization
First-Frame Propagation
ID delete
IP Swap
nomos in aking jackat he abouldans
charber
Mudl--Midal Conditions
Source Video
Malti-Mudal Conditions
Generated Rosuls
Diverse Video Editing Tasks
Unified In-Context Video Editing
Emergent Task Composition
Re- cac-ame Control Stylixation
IP insert Stylization
Bale
Skyle
Souree Vidro
Mulii Modal Canditioss
Canertted Realls
Source Vidao
Madi-MMdal Canditions
Generated Resalts
Figure I: Unified In-Context Video Editing enables unified video editing and emergent task com- position. Here we show the unification of six representative tasks, including ID Insert/Delete/Swap. Re-Camera Control, Stylization, and Propagation.
steps and cost. Another stream generally employs adapter-based designs (Ku et al. 2024 Jiang al. 2025 Wang al. 2024b Zhang al., 2023b Mou al. 2024) to inject different conditions, including reference video and multiple control signals. Despite promising progress, these methods suffer from two main challenges: 1) the adapter-based designs require modification to the model architectures and introduce parameter redundancy: and 2) these methods are generally task-specific, requiring training separate modules for each condition signal, raising difficulty for task extendability and unification. Very recently, VACE (Jiang et al. (025) tries to categorize condition signals into frames and masks for unified video editing, yet still requires heavy adapter designs and is limited to process only visual conditions.
Based on these problems, this paper presents a unified and efficient framework for video editing tasks from multi- modal signals, named UNified In-Context Video Editing (UNIC), Inspired by the recent advancements in large language models and visual content generation Yang et al. 2024a
Bai et al. 2024b 2025h Wang al. 2025d Chen et al. 2024c; Song et al., 2025 Tan al. 2024 Xiao et al. 2024b), our key insight is to integrate diverse input signals from various editing tasks as combined token sequence along the frame dimension, which are jointly modeled using the native transformer attentions to learn editing tasks from diverse context conditions. As shown in Fig. 2 to achieve unified video editing, UNIC formulates the inputs of different video editing tasks three kinds of tokens. i.e., 1) the VAE tokens from reference video. 2) the multi modii--mddl condition signals that vary upon the editing tasks, as well as 3) the noisy video latent. By jointly concatenating these tokens and dynamically varying the condition token "in context", UNIC can flexibly perform diverse editing tasks without any architectural changes.
Crucially, directly concatenating these diverse input tokens presents unavoidable challenges for unified video editing. Firstly, the multi-modal conditions from different task types present inconsistent lengths, raising difficulty in achieving correct alignment with the video. For example, camera poses usually have a direct frame-to- to-frame correspondence to each video frame, while the style images directly affect the entire video. Such inconsistency makes it challenging to deal with varying- length video editing and leads to inevitable index collisions. Therefore, we propose Task-aware RoPE, which dynamically assigns unique Rotary Positional Embedding (RoPE) indices based different task
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Vidco
Laikol Mahiosh
Mulioandl Sipnul
Malli modia
Signal
Difusiion Model
Plstoo
Diffusion Model
Contel
Mutllmodii
Model
Malti mode
DOIM Toeamaa
Conted
Beienee
Reiens
10000-00
lopea
Nake
Jnvorted
Sxpar
Notes
Mudi
Sapat
Token
Note
Tolker
(a) Extra Stage
(b) Extra One-to-One Control Modules
(c) Unified In-Context Video Editing (Ours)
Figure 2: Architectural comparison for incorporating conditioning signals. (a) Extra Stage:
Utilizes DDIM inversion reference video to derive inverted noise. (b) Extra One-to-One Control Modules: Employs dedicated, separate modules to process each control signal (c.g.. reference video, multi-modal signals) and inject guidance into the diffusion model. (c) In-Context Video Editing (Ours): Our proposed method directly integrates guidance by tokenizing all conditioning signals (reference video, multi-moda signals) and concatenating them with the noisy input tokens, allowing the diffusion model to process all information jointly within its input sequence.
types, ensuring coherent temporal understanding regardless varying condition length. Furthermore, different editing tasks may share the same modality of conditions (c.g., . an image may represent an object identity in object editing or style in video stylization). leading to task confusion. To this end, we introduce learnable condition bias for multi-modal condition signals, which enables the model adaptively learn the target task type and resolve task ambiguity.
To validate the performance of our proposed framework, we construct unified video editing benchmark incorporating six representative video editing tasks with distinct editing area ratio and conditioning modalities, including: local editing tasks of ID> Swap/Delete//nser (Jiang et al.) 2025 Bian et al. 2025b); global editing tasks of stylization (Ye et al. 2024) and propagation (Liu et al. 2024 and the re-rendering task of re-camera control (Bai et al. (025a). These tasks exhibit significant variation in their input modalities (including text, images, and camera poses). Experimental results indicate that, despite the wide range of differences among these tasks, our framework not only successfully unifies them but also delivers superior performance across all. As shown in Fig. [] method offers two distinct advantages: 1) supporting variety of editing tasks within single framework without necessitating architectural changes, showcasing high flexibility; and 2) emergent capability to combine various editing tasks, highlighting its potential unlock more complex and creative editing possibilities. We also provide in-depth analysis for unified video generation, respectively about the advantages of unified training over single task training as well as the training order of different tasks.
RELATED WORK
2.1 VIDEO EDITING AND RE-RENDERING
Based on current excellent T2V base models like Wan (Wang et al. 2025a). LongCat(Team et al. 2025), the editing capability of these models is being actively explored and has attracted signifi- cant attention. Video editing encompasses diverse tasks, ranging from local adjustments to global transformations. Achieving these modifications often requires injecting multi moltil signals, such as
motion (Tu et al.
2025) Wang et al. 2024c 2025b Xiao et al. 2024a Wei et al. 2024), style (Liu
et al.
2023
Ye al.
2024] Yang et al. 2025b). object attributes (Kogg et al. 2024: Huang et al.
2025
He et al.
2024
Zhang al. [2025), audio (Kong et al.
2025b Yang al. (025a), or camera
pose
Bai ct al.
2025a 2024a] Wang et al. 2024c| Luo et al.
2025). into generation process.
To preserve information from the reference video during editing, several methods employ DDIM inversion. This technique initializes generation noise based on the reference video and injects features extracted during inversion into the denoising steps. For instance, VideoP2P (Liu et al. (0024b) copies inversion features and replaces specific cross- cross-attention maps to align with editing requirements. FLATTEN (Cong al, 2023) utilizes optical flow to identify keypoints and inject their features to
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maintain motion fidelity, Any V2V (Ku et al. 2024) leverages spatial, temporal, and CNN features
gathered during inversion. While these approaches excel at retaining reference video information,
they require additional inversion stage, increasing inference cost and computational overhead. To
reduce computational cost, image editing works like FlowEdit (Kulikov et al. 2025) and InfEdit (Xu methods. Similarly, video editing methods like FlowDirector CCai 2025 Zhang & Han; Li et al. (2025b) follow this insight and apply it to video editing. However, they can only perform limited video editing scenarios like text-based attribute editing, while IF-
et al.
2024) explore inversion-
et al.,
V2V (Kong et al. 2025a) can only perform I2V propagation, which is not sufficient to cover and address various video editing tasks.
Instead of additional processing stages, another strategy focuses on injecting control directly into the denoising network using auxiliary modules. Some works adopt an extra model to process diverse conditions, for example VEGGIE (Yu et al. (0255) uses grounding model for processing, while Uni UniVideo (Wei et al., 2025) adopts an L.MM as text encoder to enhance the generation performance. Another stream of work assigns control module for each condition. For example, to maintain structural or layout information of reference videos, Follow-your- Canvaa (Chen et al, 2024a) extracts window details with layout encoder, while MagicEdit (Liew et al. 2023) leverages the depth video of the reference video via a depth ControlNet (Zhang et al. 2023a). a. For finer-grained preservation
of content and motion details, VideoAnyDoor (Tu al. 2025) and Revideo (Mou et al., 22224 use separate encoders to obtain the feature and inject them through ControlNet. To incorporate additional multi-modal conditions, VideoAny employs an extra ID encoder, infusing identity information via cross-attents cross-attention. Likewise, StyleMaster (Ye et al.) (024) introduces a dedicated style encoder, injecting style features through cross-attention. A common choice of these methods is the reliance on specialized control modules for each condition type. This design choice, requiring additional control modules for different conditions, inevitably increases overall model complexity and limits the extensibility to diverse novel video editing tasks.
2.2 UNIVERSAL GENERATIVE MODELS
Developing unified "omni" solutions capable of handling diverse generative tasks within a single
model is challenging but highly valuable goal. The field of image editing and generation has witnessed a clear trend towards such unification. Early works like Instruct-imagen (Hu et al. 2024)
integrated multi-modal instructions using cross-attention. OmniGen (Xiao et al., 2024b) further advances this by tokenizing all conditions as direct inputs to the transformer, creating a flexible fany-purpose" generation model without external plugins. This powerful approach of handling conditions directly within the core architecture has been validated and extended by subsequent research, including OminiControl (Tan et al. 2024). ACE (Han et al. 2024). UniReal (Chen et al. 2024b), and other unified models (Le et al. 2024 [Sogg et al. [025 Mou et al. 2025).
In contrast, unified video generation approaches often depend on task-specific control mecha- nisms. VideoComposer (Wang et al. (024b) employs different ControlNets for various inputs, and VACE (Jiang et al. (2055) uses specialized control blocks. Drawing inspiration from image edit- ing paradigms that leverage full-attention mechanisms replace dedicated modules like ControlNet, we propose extending this strategy to vidco editing. Although FullDiT (Ju et al., 2025) demonstrated 3D full-attention potential for multi-control video generation, the leap more difficult video editing remains an open challenge. Our work aims to fill this gap by developing more unified and flexible method for general video editing purposes, eliminating the need for separate control modules.
METHOD
Towards general video editing, we first review the diverse video editing tasks, and systematically define all inputs across different tasks into three basic types. Building on this, we introduce an in- context video editing framework, offering parameter-efficien and highly flexible approach adaptable to various editing purposes. We further elaborate on the specific design within our architecture for task differentiation and flexibility.
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3.1 MULTI-MODAL DRIVEN VIDEO EDITING TASKS
Multi-modal conditions driven video editing tasks, as illustrated in Fig. incorporating various inputs. For example, stylization requires a reference video and style image. while object insertion needs reference video, text, and an object image. Fundamentally, these tasks are driven by unique combinations of multi-modal inputs. We generalize these imputs into three basic types: 1) Noisy tokens represent the initial latent state of the target video, typically from random noise noise- added input video latent. Reference video tokens represent the VAE tokens of reference video, providing crucial temporal context, motion, and visual content. Their influence varies with the task's s alignment requirements. For strict-alignment tasks like ID deletion or stylization, these tokens enforce strong frame-by frame correspondence, ensuring the output precisely follows the motion and unedited content. In soft soft-reference scenarios such as re-camera control, these tokens provide more abstract guidance, following the overall content motion style without demanding strict pixel-level matching, which allows for significant deviations. 3) Multi-modal condition tokens include all other forms of guidance signals. This versatile category includes image tokens and auxiliary control tokens. For example, image tokens encode reference images, guiding structural edits (like the edited first frame in video propagation) or serving as style reference (in stylization) and ID reference (in ID insertion). Besides, auxiliary control tokens can contain diverse signals like camera poses, depth maps, segmentation masks, human pose skeletons, edge maps, sparse trajectories for motion guidance, and audio signals for lip-syncing, providing fine-grained control.
This classification allows any video editing task to represented within our structured approach, the specific combination can leads to different tasks, making it easier to uniformly process these tasks within a unified framework.
3.2 UNIFTED IN-CONTEXT VIDEO EDITING
We introduce Unified In-Context Video Editing (UNIC), simple yet effective paradigm for diverse video editing tasks. The core idea is to represent all inputs, including the noisy tokens, the reference video, and all other multi-modal conditioning signals (images, cameras, etc.), as a single, unified token sequence. This contrasts with approaches that inject conditions via additional control modules. By processing all information jointly within the full-attention layers, the model can flexibly learn the contextual relationships for different editing tasks from the conditions provided "in context".
Preliminary. Our method inherits the video diffusion transformers trained using flow matching. Specifically, the training objective is given by:
(F) = E, m. a0 (2,, t) - (x) =0))1
(I)
where 21 p(a)) represents the video sample, æp ~ (0, 1) is a Gaussian sample, and is randomly distributed in [0, 1]. The function vy the neural network that takes the noised version
2e = t + (1 t) xo as input.
Training with this loss function leads to the following ordinary differential equation (ODE):
dx:
(x( t),
dt
which allows to sample synthesized video i, from random Gaussian noise Tg-
Our transformer consists of several DiT blocks, where each block contains 2D self-attention to learn spatial information and 3D self-attention to fuse spatio- nemto-temmporl information.
3.2.1 IN-CONTEXT VIDEO EDITING
For video editing and re-rendering tasks, given reference video Vre, [exxxx and set of conditions {Ca i 1. ...n), goal is generate target video Vtar E fxxxxxx that aligns with these conditions while preserving required content of Vref-
Instead of using DDIM inversion adding add additional control modules like previous work, propose a unified and parameter-efficient approach, concatenating all the inputs along the frame dimension perform self-attention. First, we encode the reference video Vref using 3D VAE
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Unified In-Context Vidco Editing
ReCane
ID-issertion-
-D Delett
Ii Swepp
-Priginaae
Styfiiattion
Reference Video Tokens
Modal Conditions Tokens
Noisy Tokens
3D VAE Encoder
Tokenizent
TokenizentN
Text
Pase
Multi-modal Signals
Unified Video Editing
Video
Divense In Context Tokens
Figure 3: Overall Pipeline of Unified In-Context Video Editing. Our framework utilizes a unified transformer architecture for video editing. The model input is created by concatenating noisy tokens, reference video tokens, and multi-modal condition tokens (task-specific controls like images), these combined tokens form single input sequence along the frame dimension. By simply modifying the multi-modal condition tokens, this framework can handle any video editing task.
encoder to obtain its latent representation 2ref- Similarly, other conditions C₁ are converted into token sequences 2, using modality sppecifcc tokenizers (e.g., the same 3D VAE for image conditions, T5 tokenizer for text, an MLP for camera pose, etc.).
As shown in Fig- D during generation, the model operates on a noisy latent Star- By concatenating multi-modal tokens as: Zcond = .. =. which is then combined with noisy token Star, reference video tokens 2ref in frame dimension into a single sequence as the model imput 2 = [ztari Zrefi Zcond]. we can simply perform full 3D attention enable the interaction of the tokens. Without requiring an intricate inversion process or task- sask-specific architectural modifications, the only requirements for this framework are the modality-specif tokenizers, which are also needed in other methods.
3.2.2 EFFECTIVE AND FLEXIBLE TASK UNIFICATION
Based in-context editing framework, theoretically we can unify all the video editing tasks. However, directly concatenating them introduces specific challenges: 1) Task Ambiguity: When different editing tasks rely conditions from the same modality (an image simple concatenation can make it difficult for the model to distinguish the target task of the conditional tokens. 2) Positional Encoding Conflicts & Inflexibility: The 3D RoPE used in video generation base models, often assigns sequential indices in the frame dimension, which will raise problems when handling different conditions. For example, style reference does not have direct correspondence with video frames, whereas camera pose requires frame-to-frame correspondence, making it difficult maintain appropriate alignments under sequential indices. The situation becomes problematic in variable-length editing, where it even struggles to clearly distinguish boundaries between reference videos and conditions. To address these difficulties. we introduce two key components: Condition Bias and Task-Aware RoPE Index.
refer to style reference ID),
Condition Bias As mentioned above, to address the task ambiguity, we propose Condition Bias, the task-specific learnable embedding that is directly added to tokens before attention computation. To be specific, for each condition the context, including the multi-modal signals and reference video, i..., 2, E {zee 22 inject learnable bias b, Rd corresponding its task type:
2; + b;.
These task-aware tokens {&r then undergo standard full self-attention. The biases implicitly guide attention by structuring token representations: tokens from the same task share similar bias-induced feature offsets, promoting intra-task alignment while maintaining cross-task distinction. The learnable embeddings are zero-initialized to preserve original token semantics, and their dimension-presenvee addition enables simple integration with existing architectures.
Task-aware RoPE Index Standard 3D Rotary Position Embedding (RoPE) assigns sequential indices to frames. For instance, in re-camera control task with frames, noisy tokens might occupy indices 0 to N 1, reference video tokens N to 2N 1, and camera poses 2N to 3/ 1. When the

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Published as a conference paper at ICLR 2026
video frame length N varies, it will be hard to find the boundaries between the conditions, causing poor frame alignment.
To address this, introduce a task-aware RoPE indexing scheme: 1) For tasks where conditional inputs have a direct Trame-to-framme correspondence with the video (c.g., reference videos, camera poses, audio), reuse the indices of the noisy latent video frames (0 to N - 1), which can help maintain the alignment. 2) For tasks that do not have such direct frame-to-frane ID images, style references), we assign indices based on base offset and task-specific offset. The base offset, m, is determined by the video length (i.e., 17t = N). Beyond this, each task t is assigned with a pre-defined, fixed task offset, Oz, and slot count (or length), L,- The value On dictates the starting point of task f's allocation relative to m, and L, defines how many consecutive indices the task occupies. The index range for such a task is calculated as:
correspondence (e.g..
Index(t) (m + O+) + [0, [O,.....
These task offsets O and lengths L; are chosen to ensure non-overlapping task slots and maintain clear task distinction. For example, if ID images (task 1) are defined with a task offset O1 = 100 and require I1- = 3 slots (supporting up to 3 IDs), they will use indices from N 100 N + 102. If a style reference (task 2) is defined with a task offset O2 = 200 and requires L2 = 1 slot, it will occupy the index N + 200. This adaptive approach ensures that task slots automatically scale with the video length N, while preserving their relative positional relationships and avoiding overlap.
Training Strategy In this work, we unify six tasks into this framework, including ID insert, ID swap. ID delete, re-camera control, video style transfer, video propagation. These tasks were chosen due to their diverse condition modalities and varying editing degree. However, joint training performance is affected by training order due differences in task complexity and convergence rates. For example, re-camera control requires 600k iterations to converge, while simpler tasks like ID-swapping achieve good results in only 80k iterations. During joint training, if we start with easier tasks, they dominate the optimization with faster loss reduction, failing to allocate sufficient capacity to more difficult tasks. Therefore, we choose hard-to-casy training strategy, i.c., progressively training tasks from higher to lower difficulty for convergence.
4 EXPERIMENTS
Dataset and Benchmark UNIC is trained on multiple datasets to support multi-task video editing. For ID swap/insert/dellte and stylization tasks, we use self-constructed datasets (see Appendix for details). These are also adapted for the propagation task by using the first frame of the target video as the edited input. For re-camera control, we use the Multi-Cam Video Dataset from ReCamMaster (Bai et al. 2025a). To comprehensively evaluate the method, we construct unified video editing benchmark incorporating six representative video editing tasks with distinct editing area ratio and conditioning modalities. The details of the benchmark can be found in Appendix.
Evaluation Metrics The evaluation of these tasks is conducted across two dimensions: task-specific performance and overall video quality. To assess task-specific performance, for ID tasks, we employ the DINO-score (Caron et al. 2021) and CL.IP-corre (Radford et al.) (02T) to evaluate identity similarity with the reference image. For the style task, style similarity with the reference style image is validated using the CSD-score (Somepalli et al.) (2024), while ArtFID and CFSD (Chung) ct al. 2024) are used to consider content preservation. Furthermore, for re-camera control, we use RotErr, TransErr, and CamMC following Caml2V (Zheng et al. 2024) to evaluate the alignment with the given camera pose. Besides, the overall quality of the generated videos is assessed by motion smoothness, dynamic degree, and aesthetic score, subject consistency, background consistency, temporal flickering and imaging quality (Huang et al., 2024).
4.1 EDITING PERFORMANCE COMPARISON
We compare our approach with the state-of-ther the- unified video editing methods like VACE (Jing et al. (025) and task-specific methods from different video editing methods, like ReCamMaster (Bai et al. 2025a). We provide an evaluation of overall video performance, including text alignment and overall quality, as well task-specific metrics such as DINO-score in ID-related tasks. Since
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Table 1: Quantitative comparison on six video editing tasks: ID Inser//wwap/Deltte, Re-Camera control, Stylization, and Propagation. Best results are highlighted in bold.
Insert
Idfenity
Aligument
Video Quality
Method
CLIP-I1
DINO-I1
CLIP-scoref
Smoothnessf
Dynamict
Aesthetict
al.,
0.110
0.100
44.568
5.407
Pika
2025
0.689
0.387
0.253
0.934
20.65
5.393
Ours
0.598
01245
0.216
0.961
11.07
5.627
ID Swap
VACE
Jiang et al. 2025
0.712
0.423
0.230
0.964
29.306
6.015
Pika p1k 2025
0.700
0,393
0.209
0.948
20:09
5.210
Any VnyV Top
et
2024
0.605
0,229
0.218
0.917
7.596
4.842
(rrop)
Ours
0.725
0.429
0.242
0.971
7,500
6.056
ID Delete
Video Reconstruction
Aligument CLIP-scoref
Video Quality Dynamic?
Method
PSNR1
ReVVideo-CIIPP
Smoothnesst
Aesthetic
A/yo2//PPop)) Ka et al.
2024
19.504
0.869
0.208
0.964
4,980
5.325
VACH Bang at 2T2S
20:947
0,883
0:211
0.966
15.441
5.332
VideoPanter
Baan
2025b
22.987
0.920
0.212
0.957
13.759
5.403
Ours(Prop)
0.906
Ours
19.171
0.217
0,970
10994
5.493
Propagation
Frume Alignment RefVideo-CI.IPt
Alignment CLIIP-ccore
Video Quality Dynamle?
Method
Smoothnesst
Aesthetic
AnyV2V Ku al.
2024
0.812
0.229
0.935
13.462
5.136
VACE(2V)
Jang et al.
2025
0.574
0.234
0.932
36.783
5.425
Ourss
0.840
0.226
0.966
12.762
$.568
Stylization
Style Content
Alignment CLIP-score
Video Quality Dynamic?
Method
CSD-Score?
ArtFID
Smoothness?
Aesthetic
Any AnyrPp( al 2024
43,299
StyleMaster
2024
0.306
38.213
0.188
0.952
9:758
5.121
Ours(Prop)
197
36.198
0.215
0.932
11.569
5.045
Ours
0.259
37,619
0.171
0.945
9.370
5.276
Re- Re-Camera Control
Camera Control
Alignment CLIP-score?
Video Quality Dynamict
Method
RotErr]
TransErr.
Smoothness! 0.917
Aesthetict
RoCamMaster- Wan Bai ot al. 2025a
1.454
5,695
0.219
31.751
4.738
the propagation version (indicated by a (prop) suffix) for Swap/Delete/Styiizaaiie tasks requires the edited first frame, we specifically use Insert-Anything (Song al., 2025). FLUX (Labs) 2024). and the first frame of StyleMaster (Ye et al. 2024) to obtain the edited first frame.
As presented in Table [ our six-in-one framework demonstrates consistent and strong performance across all evaluated video editing tasks. Notably, our model achieves leading results in ID Insert and Re-Camera Control, outperforming existing methods on most metrics. For Stylization, we achieve comparable performance to specialized models like StyleMaster (Ye et al. 2024). While for ID Delete, specialized models like VideoPainter (Bian et al.) 2025b) show better video reconstruction with PSNR, approach still surpasses in alignment with CLIP- and some vidco quality aspects such as Smoothness and Aesthetic score. Furthermore, a significant advantage of our method is its ficxibility, supporting arbitrary resolutions and lengths, capability not present in many fixed-length video editing models.
4.2
COMPARISON WITH OTHER CONDITION INJECTION METHODS
To further demonstrate the difference with other conditioning and tuning methods, we implement both LoRA and adapter-based versions, with the latter following the design of VideoAny (Tu et al.
2025) for ID insert and ID swap tasks. The computational cost and performance are shown in
Table 2 and Table Our method demonstrates superior performance compared to other approaches, consistently outperforming both LoRA and Adapter-basse methods across all metrics in both ID Insert and ID Swap tasks. Moreover, with a simple step cache optimization, our approach shows significant efficiency potential while maintaining competitive performance.
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Table 2: Computational cost comparison with LoRA and adapter- based methods on ID Insert task.
Table 3: Quantitative comparison with LoRA and adapter methods.
Method
FLOPs
Params
CLIP-I
DINO-I
CLIP-Score"
CLIP-I
DINO-I
CLIP-Sromi
Adsper-tneel
0.503
0.190
0.158
Adapter hased
51.73T
1.65B
0.505
0.190
0.158
LoRA
0.37
0.222
0.195
LoRA
69.29T
1,208
0.537
0,222
0.193
Oun
0,588
0.251
0227
Full Finctuning
69 29T
1,208
0.528
0.242
0.203
ID Swap
CLIP-I
DINO-I
CLIP-Srore)
Ours
69.29T
1.208
0.568
0.254
0.227
LoRA
Adapter based
0,713
0.894
0.417
0.433
0,230
0.21
Ours step cache
39.28T
0.558
0.249
0.232
0.449
Table 4: Ablation study on the training order of different tasks. We employ three settings: hard to easy, easy to hard, and joint training. Best results are highlighted in bold.
Identities
ReCam TransErr)
Style ArtFID,
Training Order
CLIP-It DINO-I;
RotErr]
CamMCJ
CSD-Score
CFSD.
>> camera
>> camera+id
0.725
0.429
1.275
5.667
6.154
0.259
37.619
0.107
->
id+sttll+pproaggttoon
0.726
0.427
1.398
5.681
6.275
0.247
37.748
0.109
>> comame-id-ssylllpoooaaaain
0,713
0.421
2.287
9.694
10.377
0.298
38.953
0.170
4.3 ANALYSIS FOR OUR IN- IN-CONTEXT VIDEO EDITING
We provide a set of experiments to demonstrate the training choice of in-context video editing for different tasks. Based on our experimental results, we summarize several key findings that highlight how specific training strategies enable robust multi-task unification and enhance the overall performance of our in-context framework.
Should we train tasks sequentially or jointly? To determine the optimal training strategy for our unified tasks, which possess varying levels of difficulty, we investigate whether sequential training or joint training yields superior performance. As shown in Table 4 we report results under different training strategies. Among our selected six tasks, re-camera control is the most difficult, since the modality far away from the visual content, while the ID-related task relatively easy to learn. We experiment with three approaches: (1) sequential training from hard to easy tasks, (2) sequential training from easy to hard tasks, and (3) joint training of all tasks from scratch. Our findings indicate that the sequential training approaches (hard-to-easy and casy-to-hard) can help the multi-task learning. contrast, joint training from scratch, while capable of learning easier tasks, struggles significantly with harder ones, resulting in poor performance the re-camera control task.
Will the task unification affect single-task performance? There is natural concern about whether multi-task learning affects single-task performance. To explore this, we conduct a comparative study between task speciffe models and the unified model. We train separate models for stylization, ID- related editing, and re-camera control, and compare their performance with the unified model. As shown in Table 5) the unified model does not impair task performance and offers advantages in camera control and style similarity in stylization. However, it slightly compromises content preservation in stylization. This trade-off is due to the training mechanism: during ID-related tasks, the model is trained to fully preserve content. As result, in stylization tasks, the unified model retains more information than just style, leading to higher style similarity but reduced content preservation. Overall, unifying diverse tasks within this framework does not significantly degrade individual task performance and can even enhance it in some cases.
Do condition bias and task-aware RoPE Matter? To validate our proposed Condition Bias and Task- RoPE, we conduct an ablation study, with results presented in Table Comparing our full model against variants clearly demonstrates their benefits. The baseline model (D1), lacking both components, performs adequately simple tasks but struggles with complex temporal tasks like re-camera control, showing high TransErr and CamMC. Adding Condition Bias alone (D2) improves CLIP-I in ID swap and reduces TransErr in re-camera control, while implementing only Task-

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Published as a conference paper at ICLR 2026
Table Performance Comparison between task-specific model and unified model. We evaluate our unified model (B4) against task-specific models trained for ID (B1), style (B2), and re-camera control (B3) on relevant metrics for each task. Best results are highlighted in bold.
Identities
Style ArtFID
ReCam TransErr.
Task
DINO-I1
CLIP-I
CSD-Score"
CFSD)
RotErr
CamMC.
B1
0.449
0.723
B2
style
0.234
37.674
0.096
B3
1.472
5.836
6.434
B4
id+styleecamera
0.429
0.725
0.259
37.619
0.107
1,275
5.667
6.154
Table 6: Ablation Study Condition Bias and Task-aware RoPE. We compare our full model (D4) with their variants. Best results are highlighted in bold. f indicates higher is better; & indicates lower better.
Condition Blas
RoPR
Identities
Style ArtFID]
ReCam TransEer.
Sequential
Task-aware
DINO-I:
CLIPII:
CSD-Scoref
CFSD
RotErr]
CamMCI
D1
0.453
0.710
0.242
34.194
0.081
2.501
8.972
13,119
35.548
0.091
6.566
13
0.422
0.710
0.258
32.768
0.072
1 1.004
6.038
6.498
D4
0,429
0.725
0,259
37.619
0.107
1.275
5.667
6.154
RoPE (D3) significantly reduces CamMC in re-camera control. The full model (D4), combining both components, achieves superior performance across all tasks. These results demonstrate how Condition Bias enables effective task disambiguation while Task-aware RoPE enhances temporal modeling, together creating robust unified video editing framework.
CONCLUSION
In this paper, we introduce UNified In-Context Video Editing (UNIC), simple yet effective frame- work that unifies diverse video editing tasks within a single model in an in-context manner. To this end, we formulate the input of different video editing tasks as three types tokens, integrating them as single unified token sequence jointly modeled with the original full-attention of diffusion trans- formers. With the devised task-aware RoPE and conditional bias, our method can flexibly perform different editing tasks and support their combination. To facilitate the evaluation, we also construct unified video editing benchmark. Extensive experiments on six representative video editing tasks demonstrate that our unified model shows superior performance on each task and exhibits emergent task composition abilities.
LIMITATION AND FUTURE WORK
Our current unification efforts are limited to the six tasks discussed. The incorporation of additional modalities, such as lip-syncing with audio (Kong et al.) 22025b) Zhong et al. 2025 Zhang et al. 2024), visual effects (Li et al. 2025a Bian et al.) 2025a), and multi-shot scenarios (Wang al. 2025c remains unexplored in this work. future research, we intend to integrate wider array of tasks to investigate whether there is upper limit the number of tasks that be successfully unified. Furthermore, recognizing that high token count during self-attention can significantly increase computational overhead,
plan explore architectural designs alternative mechanisms to improve efficiency (He et al., (2025). Also, reinforcement learning can be used to enhance the
generation quality and control accuracy (Liu al. 2025 2026
Ye et 2025
Qin al. 2025).
7
ACKNOWLEDGMENT
This work was supported in part by the National Natural Science Foundation of China (Grant No. 62372480), and in part by the 2025 Tencent AI Lab Rhino- Focused Research Program.
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Zixuan Ye, Quande Liu, Cong Wei, Yuanxing Zhang, Xintao Wang, Pengfei Wan, Kun Gai, and Wenhan Luo. Visual-aware cot: Achieving high-fidelity visual consistency in unified models. arXiv preprint arXiv:2512. (tt:::251121968850 19686, 2025.
Shoubin Yu, Difan Liu, Ziqiao Ma. Yicong Hong, Yang Zhou, Hao Tan, Joyce Chai, and Mohit Bansal. Veggie: Instructional editing and reasoning video concepts with grounded generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15147-15158, 2025.
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Jiacheng Zhang and Kai Han. Veflow: Training-fres text to video editing via inversion- video editing flow.
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 3836-3847, 2023a.
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Bojia Zi, Shihao Zhao, Xianbiao Qi, Jianan Wang. Yukai Shi, Qianyu Chen, Bin Liang, Rong Xiao, Kam-Fai Wong, and Lei Zhang. Cococo: Improving text-guided video inpainting for better consistency, controllability and compatibility, In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 11067-11076, 2025.
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A APPENDIX
The Appendix of this paper contains the following main sections:
Additional Experiment and Analysis providing supplementary experimental results and detailed analysis
UNIC Benchmark describing the benchmark details
-escrian
Training Dataset Construction the construction of training datasets
Training Schemes explaining the specific training strategies and protocols
Limitation and Future Work discussing current limitations and future research directions
Statement for Large Language Models clarifying the usage of large language models
B ADDITIONAL EXPERIMENT AND ANALYSIS
B. VIDEO QUALITY COMPARISON ON DIFFERENT EDITING TASKS
Here we give a more comprehensive report on the generated video quality compared with the baseline methods, as shown in Table Our method demonstrates consistently stable video quality across all editing tasks. The results show that our approach maintains reliable performance metrics across different scenarios, including identity swap, style transfer, and camera control tasks. The temporal quality remains stable with competitive motion smoothness and minimal flickering artifacts. Additionally, our method exhibits robust consistency both subject and background preservation while delivering satisfactory imaging quality.
Table 7: Video Quality Comparison on different editing tasks. Best results are highlighted in bold.
Motion Smoothness
Dynamic Degree
Aesthetic Score
Subject Consistency
Background Consistency
Temporal Flickering
Imaging Quality
ID Swap
VACE Jiang
2025
0.990
0.350
0.613
0.966
0.957
0.982
0.697
(pdk 2025
0.994
0.517
0.973
0.960
0.708
Prropp Ours
2024
0.966
0.150
0,499
0.873
0.909
0.957
0.552
0.993
0.100
0.605
0.975
0.970
0.988
0.690
Any V2V(rop))
2024
0.433
0.858
0.924
0.375
StyleMaster (e ct Ours
2024
0,988
0.400
0,490
0.941
0,956
0.978
0,499
0,989
0.300
0.551
0.929
0.948
0.974
0.604
Re-Camera Control
ReCammaster-- Wan (Bai et
2025a
0.994
0.500
0.459
0.930
0.921
0.983
0.576
0.994
0.600
0.449
0.925
0.942
0.604
B.2 ADAPTION TO OTHER BASE MODEL
We also adapt our method another base model Wan-1.32 (Wang et al. 2025a). which has a similar model size and comparable T2V performance to our base model. The experimental results are shown in Table g We evaluate our method on three different tasks: ID insertion, stylization, and camera control (ReCam). The results demonstrate that our approach achieves comparable performance across both base models. While the Wan 1.3B model shows slightly lower performance some metrics, this is likely due to the fact that did not further adjust the training parameters specifically for this model. Nevertheless, these results demonstrate that our method is model-agnostic and successfully unifies these diverse video generation tasks across different base models.
B.3 BENEFITS OF UNIFICATION
Our approach demonstrates significant advantages over task-specific adapters or task-specific tuning like LoRA adapters. We analyze these benefits from three key perspectives:
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Table 8: Performance comparison on different base models.
Task
Type
CLIP-T
DINO-IT
CLIP-ScoreT
Insert
Internal
0.598
0.245
0.216
ID Insert
Wan 1.3B
0,528
0.210
0,193
Task
Type
CSD-scoref
ArtFIDL
CLIP-Scoref
Stylization
Stylization
Wan 1.3B
0,236
35.318
0,181
Task
Type
RotErr]
Transerr]
CLIP-Scoref
5.667
ReCam
Wan 1.3B
1,272
5.674
0,218
Parameter Efficiency Our method achieves remarkable parameter efficiency by adding only thousands of trainable parameters to support six distinct tasks. In contrast, using separate LoRA modules would require training and storing multiple task-specifi modules, each containing millions of parameters, resulting in substantially higher storage and computational demands.
Multi-task Synergy The shared architecture enables positive interactions between tasks through cross-task information sharing. This particularly evident in addressing domain-specifie limitations. For example, in re-camera control tasks, multi-task joint training significantly improves human representation quality compared to single-task training, as shown in Table The multi-task approach achieves better scores in human-anatomy -naatmy (0.903 vs 0.889). numan-identig (0.887 vs 0.844), and human-clothes (0.921 vs 0.891), demonstrating how shared knowledge across tasks helps mitigate domain-specific constraints.
Table Comparison of human-centered metrics between ReCam-only training and multi-task joint training.
Task
Training Strategy
Human-Anen Anatomy?
Gluman-Identiog
Human Clothesf
ReCam
ReCam Only Training
0.889
0.844
0.891
ReCam
Multi-Task Joint Training
0.903
0,887
0.921
Efficient Fine-tuning The unified architecture facilitates rapid adaptation to new tasks through shared representations. Our experiments demonstrate the efficiency knowledge transfer, as shown in Table 10 When fine-tuning for propagation tasks based on ID swap knowledge,
achieve superior performance (RefVideo-CLIPP 0.820) with just 0.4k training steps, compared to 2k steps required by propagation-only training (RefVideo-Ch) 0.792). This 5x reduction in training steps, along with consistent improvements across multiple metrics, validates the effectiveness our unified framework.
Table 10: Comparison of fine-tuning efficiency and performance metrics between different training strategies.
Strategy
Training Steps
RefVideo-CLIPT
CLIP-score
Smoothness
Dynamic
Aesthetic
propagation only propagation only based on ID swap based on ID swap
lk
0.765
0.205
0.948
12.956
5.028
2k
0.792
0.219
0.952
13.262
4.912
0.2k
0.789
0.217
0.951
13.245
5.036
0.4k
0.820
0.226
0.956
13.562
5.265
These benefits enable not only more efficient model deployment but also better task performance and casier extension to new video editing tasks. Moreover, unlike methods requiring multiple LoRAs, our unified approach allows for seamless task composition without the risk of conflicting behaviors when combining different editing operations.
B.4 SCALING UP MODEL SIZE
We conduct comprehensive experiments to investigate the impact of model scaling various video editing tasks. Our analysis compares the performance between 1B and 10B parameter models across different tasks and evaluation metrics, shown in Table 12
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Table 11: Comparison with different conditioning and tuning methods.
Lightweight
Efficient Finetuning
Task Composition
LoRA
X
Adapter
X
XX
Ours
Table 12: Performance comparison between 1B and 10B parameter models across ID tasks.
Task
Type
CLIP-IT
CLIP-ScoreT DINO-If
ID Insert
IB
0.598
0,245
0.216
ID Insert
10B
0.632
0,287
0.246
ID Swap
IB
0.725
0,429
0.242
ID Swap
10B
0.731
0,447
0.238
Task
Type
PSNR1
RefVideo-CLIPT
CLIP-Score"
ID Delete
1B
19.171
0.900
0.217
ID Delete
10B
47.850
0.888
0.208
The results demonstrate significant improvements when scaling up larger models. For the ID Insert task, scaling to 10B parameters yields substantial enhancements across all metrics, with CLIP-I improving by 5.7% and DINO-I increasing by 17.1%. Similar improvements Swap tasks, while Delete shows remarkable enhancement in reconstruction quality with PSNR increasing from 19.171 to 47.850. These results provide strong quantitative evidence that scaling up model size leads to better performance across various video editing tasks.
observed in ID
B.5 COMPUTATION COST ANALYSIS
We provide detailed breakdown of the computational requirements for different video editing tasks in our unified framework, as shown in Table 1
Table 13: Detailed computational requirements for different video editing tasks.
Tisk
Memory Consumption
Inference Time/Iter
Number of Tokens
Dencising Steps
Video Resolution
24,45G
1993258
30
1D Swap
24.456
2.45s
(43, 1008,
1992)2
30
672x384477
Delete
24.45G
2.45s
1993)289
30
672x384x77
Propagation
22. 22.23G
2.31s
(41, 1008, 47629847
30
672x384x77
Stylization
22.23G
2.31s
4752)947609856
30
672x384x77
Re-Camera Control
22.16G
3.66s
(60, 1008, (152)x696729600
30
672x384x77
B.6 ANALYSIS OF JOINT TRAINING PERFORMANCE
We investigate the performance degradation observed in joint training and propose potential solu- tions. Our analysis reveals that the degradation primarily stems from the varying complexity and convergence rates across different tasks. Specifically, re-camera control requires approximately 600k iterations to converge in single-task training, while simpler tasks like ID-swapping and stylization achieve satisfactory results within 80k iterations.
To address this issue, we experiment with an imbalanced sampling strategy that allocates more training resources to the complex re-camera control task (0.7) while reducing the sampling probability for simpler tasks 10.1 each). The results show that while this approach improves re-camera control performance, leads to degraded performance simpler tasks, suggesting that achieving optimal performance across tasks of varying complexity remains challenging in joint training scenarios.
B.7 ANALYSIS OF TASK INDICATION METHODS
We investigate different approaches for indicating tasks in the model input, comparing text- text-based task indication with task-aware rope design. Specifically, explore three settings: (1) pure text
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Table 14:
Performance comparison between balanced (0.25 each) and imbalanced (ReCam: 0.7,
others: 0.1) sampling strategies in joint training.
Train
ID Swap
ReCam TransErr
Style
Strategy
CLIP-It
DINO-If
RotErr]
CamMCJ 10.377 8.040
CSD-Scoref
ArtFID
balanced
0.713
0.421
2.287
9.694
0.298
38,953
imbalanced
0.712
0.409
2.010
7.279
0.192
39,453
indication where we prepend task type the caption (e-g.. "[stylization] A woman is...", (2) our original design with task-aware rope and condition bias, and (3) combined approach incorporating both mechanisms.
Table 15: Performance comparison of different task indication methods. Text Indication refers to indicating task type caption. Ours means using task-aware RoPE and condition bias. Combined refers to indicating both through text and our design.
Meihod
Text
Tusk-aware Rope
Condition Bias
ID Swap
Stylization
ReCam
Indication
DINO-17
Retvidco-CLiPt
CSD-Sooret
ArtRDD
RotErr]
TransErr
AT (Text Only)
0,432
0.723
0.294
42.080
1.622
6.613
(Ours) A3 (Combined)
0.429
0.776
0.259
37.619
1.275
5.667
0.441
0.733
0.304
40.771
1.653
6,416
While text-based task indication (AI) shows improved condition image similarity metrics (DINO-
I and CSD-Score), it compromises video consistency (lower Refvideo-CLIP) and ReCam task performance. The combined approach (A3) also underperforms compared to our original design (A2), suggesting that explicit text indication may interfere with the model'ss s ability balance instruction understanding and content preservation. This limitation might be addressed through large-scale pre-training.
C UNIC BENCHMARK
To comprehensively evaluate performance, we create a unified benchmark of six tasks, each containing 20 to 50 carefully designed evaluation cases.
C.1 ID INSERT
collect 20 videos from Artgrid art 2025) as source video, and we carefully select suitable ID for each video to insert, also ensuring that the semantic is reasonable. As shown in Fig.S our selected ID includes both clean object without background and complete picture with background.
Source Video & ID & Caption
edge of the sea.
Example target video
Figure 4: ID Pool and example of ID insert evaluation cases.
C.2 SWAP
For this task, utilize 14 videos from VPBench (Bian al. 2025b) and 6 videos online as the source videos. With the source videos, the objects to be swapped were segmented using SAM2 (Ravi et al. 2024). Then, carefully design and choose the object place in. As shown in Fig.
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appropriate ID was then selected from our ID pool to replace the segmented object, and caption was generated to describe the final target video.
Source Video
Segmented Video
ID Caption
Example Target Video
the young man with dark hair and contemplative expression now wearing stylish leather jacket.
Figure 5: Example of ID swap evaluation cases.
C.3 DELETE
For ID delete, we expand the 10 videos in VPBench (Bian al. 2025b) 20 by additionally collecting 10 videos. Also, SAM2 (Ravi et al., 2024) is used to segment the object to be deleted. Then generate caption for the target video. The example is shown in Fig.S6
Source Video
Masked Video
Example Target Video
Figure 6: Example of ID Delete evaluation cases.
C.4 STYLIZATION
For the stylization task, we collected 12 representative styles serve references. These include diverse artistic expressions such as pixel art, oil painting, Chinese painting, and line art, among others. Examples of these styles are illustrated in Fig.S7 Then randomly select 50 videos from as the source videos.
Artgrid (art 2025)
Style Pool
Source Video
Style Ref Image
Example Target Video
Figure 7: Example of Stylization evaluation cases.
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C.5 PROPAGATION
For the propagation task, we expand the 38 example of GenProp (Liu et al. 2024a) to 50 examples by adding 12 stylization propagation test cases. Example shown in Fig.S8
Source Video
Edited First Frame
Example Target Video
Figure 8: Example of propagation evaluation cases.
C.6 RE-CAMERA CONTROL
To evaluate the re-camera control task, utilized 10 basic camera trajectories and 50 randomly selected videos from Koala (Wang et al. (0224). Each of the 10 trajectories was then applied to distinct videos from this set (totaling 50 traeectory-video pairs).
D TRAINING DATASET CONSTRUCTION
This section details the construction of our datasets for the six tasks.
D.I ID-RELATED TASK
To generate training data for ID-related tasks such deletion, swap, and insertion (as illustrated in Fig. $9), we first use SAM2 (Ravi et al., 2024) to obtain an object's segmentation mask from the source video. This mask is then applied with cv2 inpaint to produce an inpainted video. However, this simple inpainting method often introduces visual artifacts in the inpainted regions. To address this, we train ControlNet conditioned on the original video, which effectively eliminates these artifacts. The resulting artifact- trifact-free video serves as the reference video for the insertion task, with the original source video as the target. Additionally, masked video, created by applying the segmentation mask to the source video, serves the reference video for both deletion and swap tasks. Using this method, we create 3000 videos for each task.
D.2 STYLIZATION
When considering how to construct the paired style video dataset, straightforward idea to use video-to-video stylization model to convert real-world videos into stylized ones. However, our experiments revealed that this approach frequently results in temporal inconsistencies, flickering artifacts, and lower visual quality.
then noted that Text-to- Video (T2V) models are capable of generating stylized videos that exhibit superior quality and maintain higher fidelity to a given reference style image. This observation led us to an alternative strategy: rather than stylizing an existing real video, we first generate a high-quality stylized video using T2V model. Subsequently, counterpart using a tile-based video ControlNet. As illustrated in Fig.STO the results confirm that this is feasible method. Using this method, create 10,000 paired videos.
transform this stylized video into realistic
D.3 PROPAGATION TASK DATASET
To construct dataset for the propagation task, we leverage existing paired data from our ID-related and Stylization task training sets. Each pair in these datasets contains a source video and a target video.
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Source Video
Segmented Result
ev2.inpaint Result
cv2.inpaint Result After
Masked Video
Deletion
Reference video
Target Video
Swap
Reference video
Target Video
Insertion
Reference video
Target Video
Figure 9: ID-related task data construction.
Stylized T2V Generation
V2V Tile ContralNet
Stylized Video
Realistic Video
Figure 10: Stylization pair data construction.
The propagation task requires input triplets consisting of a source video, a target video, and the first frame of that target video. We generate these triplets from our existing paired data in two distinct configurations. In one configuration, the original source video serves as the propagation source, the original target video serves as the propagation target, and use the first frame of this original target video. Alternatively, the roles can be reversed: the original target video can serve as the propagation source, with the original source video becoming the propagation target, and we would then use the first frame of this (now) target video. This strategy effectively allows us to derive greater volume of propagation training data from our existing resources. Specifically, we combine 9,000 samples from ID-related tasks and 10,000 samples to form a 19,000-sampp dataset.
D.4 RE-CAMERA CONTROL TASK DATASET
For the Re-Camera control task, we employ the Multi-Cam Video dataset from the ReCamMaster (Bai This established training dataset provides 136,000 videos.
al. 2025a).
E TRAINING SCHEMES
E. MODEL DETAILS FOR SIX TASKS
Figure ŞII illustrates the model architecture details for handling the six distinct tasks. The configura- tion for each task, particularly concerning input encoding and Rotary Positional Encoding (RoPE) indices, is as follows:
ID Insertion and Swapping: For these tasks, the injected ID image(s) are encoded using the same 3D VAE Encoder employed for the reference video. Their RoPE indices commence
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ID Insert/Swepp
Propagation
Newwy Tilecn
Infrrme Vike Tekre
Neta Tikces
Iefoeme Vikeo Tokns
Linghe
coodiee
Polate
Re-camerra Control
Nelow Tekens
Rebermes Video Toices
ODO
Your dlfet
Loadh
Alge
Stylbaation
Sayle Image
Inender
Neiay Tebees
Refemue Vide Taken
Tak Dilec 36
Condtian
000D00
Figure 11: Detailed Input and RoPE index for the six tasks.
from a base offset of 6, with an additional task-specific offset of 100. This allocation has a length of 3, accommodating up to three ID images. If fewer than three ID images are provided for given instance, the embedding slots for the remaining IDs are filled with representations corresponding to black images.
ID Deletion: In the ID deletion task, all input ID image slots are effectively treated as black images (i.e., their embeddings correspond black images), signaling the removal operation. The RoPE indexing follows the same base offset of 6 and task offset of 100 as ID insertion/swapping..
Stylization: For the stylization task, the style reference image also embedded using the 3D VAE Encoder. It utilizes the same base RoPE offset of 6, but with different task-specific offset 200. Therefore, RoPE indices for style tokens begin at 206 (i.e., 6 + 200).
Propagation: The propagation task leverages the direct correspondence with the first frame of the target video. Consequently, the tokens representing this first frame (used as the propagation source/reference) are assigned RoPE index
Re-camera Control: Camera parameters for re-camera control, initially provided as tensor of size F' x 3 4 (where the number of frames), undergo a specific tokenization process. First, the last two dimensions (3 x 4) are flattened, resulting in F x 12. These features are then spatially padded to match the VAE token dimensions
H/4 W/4 to obtain F 12 H/4 W/4. Subsequently, an MLP embeds these processed parameters into tensor of shape of tokens encoded by the 3D VAE. Crucially, since this task regards the reference video primarily as soft guidance rather than requiring strict pixel-to- ixxe-to-pixel alignment, the RoPE indices for the reference video tokens are shifted by +30 from their original positions (e.g.. original index becomes 300).
emb him_ H/4 W/4, aligning them with the dimensionality
E.2 TRAINING PROGRESS
As shown in Fig.ST2 T2 we present two training progression strategies for our method: one starting with tasks deemed "hard" and progressing to "easy" ones, and the converse strategy, from "easy" to "hard". The classification of tasks as "hard" or "easy" is based training within this in-context framework. For instance, we found that the re-camera control task typically requires training approximately 100k data volume to reach satisfactory performance, whereas ID-related tasks (such as ID insertion or swapping) achieve comparable results with only about 20k volume. Consequently, categorize re-camera control "harder" task in this context.
empirical observations during
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Easy Hard
Hard to Easy
Stage
Swaett
ReCam
(9K)
Stage
Style [10X]
RoCam (1368)
[Insert-Delete-an Swap)
(91)
(Ineert-Oeltte-Sappp
[10K]
Style
Prop (19K)
ReCam (1364)
ReCam (1551)
[mmert-DeiettSSwapp
Figure 12: Training progress with different settings. We demonstrate two settings of the progress and the data volume of each task.
E.3 TRAINING DETAILS
UNIC is trained on multiple datasets to support multi-task video editing. All finetuning experiments start from internal pre-trained model with IB parameters and 28 sequential standard Diffusion Transformer (DīT) blocks. Each block contains 2D self-attention, 3D self-attention, cross attention and FFN layers.The The model is finetuned for 16k iterations on 32 H800 GPUs with batch size of 64. We only finetune the transformer module and the new tokenizers (like the MLP for the camera pose), while freezing the 3D VAE, Text T5 Tokenizer.
F STATEMENT FOR LARGE LANGUAGE MODELS
We utilize large language models (LLMs) this paper only for the purpose of grammar correction and text refinement. The LLMs are not employed for generating original content or contributing to the conceptual development the ideas presented.
25


Reviews:
[{"review_id": "WMEvQMYn1g", "reviewer": "Reviewer_njmg", "summary": "The paper proposes UNIC (UNified In-Context Video Editing)， a framework for unifying diverse video editing tasks (e.g., ID insertion/deletion/swap, stylization, re-camera control, propagation) within a single diffusion transformer model. \nInstead of relying on task-specific adapters or DDIM inversion, UNIC concatenates noisy video, reference video, and multi-modal condition tokens into a single token sequence, enabling unified learning through native transformer attention. Experiments on a unified benchmark of six tasks show that UNIC achieves competitive or superior results to baselines like VACE, AnyV2V, and ReCamMaster, while offering emergent task composition and parameter efficiency.", "questions": "please see weaknesses section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of in-context unification across multiple video editing tasks via token concatenation is elegant and clear\n\n2. The introduction of Condition Bias and Task-Aware RoPE is well-motivated and clearly implemented.\n\n3. The benchmark spans six distinct tasks, covering both local (ID editing) and global (stylization, propagation) settings. it is comprehensive .\n4. The demonstration of task composition is interesting, highlights that the model generalizes beyond discrete training tasks", "weaknesses": "1. The paper doesn’t compare against or reference newer state-of-the-art T2V and editing models like Wan series\n\n2. While the in-context formulation is elegant, much of the architecture directly borrows from existing full-attention DiT and OmniGen paradigms. The novelty primarily lies in combining these ideas for video, rather than a fundamentally new mechanism.\n\n3. Missing some important related work discussion like VEGGIE [1], which is also unified video editing framework + abilities in in-context video editing \n\n4. Some metrics (e.g., CLIP, DINO, ArtFID) are weak proxies for perceptual quality. The paper doesn’t include human evaluation or temporal consistency metrics (like VBench perceptual coherence, even though it is for video generation, it still captures the generation quality of a video editing model).\n\n[1] VEGGIE: Instructional Editing and Reasoning Video Concepts with Grounded Generation. ICCV25.", "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": "EIZsifZaNw", "reviewer": "Reviewer_MK6C", "summary": "This paper proposed UNIC, a finetuning framework that enables a video diffusion model to perform a wide range of video editing tasks in an in-context fashion without task-specific modules, adapters, or dedicated inversion pipelines in prior works. A unified video editing benchmark is built and reported UNIC achieves comparable performance with task specialists on the benchmark.", "questions": "Q1) W2 also raise an interesting question: If such a framework is finetuned on CogVideoX and also Wan 2.2, is it we should expect models with stronger pretrained video diffusion model would perform better? Or the gain is heavily relied on the finetuning? Would it be possible to share some empirical results?\n\nQ2) Why the in-text citations style are inconsistent across the paper? \n\nStyle 1 in Introduction: \"Current video editing methods primarily follow two strategies to inject reference video and control signals. As depicted in Fig. 2, one stream of methods, represented by Video-P2P (Liu et al., 2024b), AnyV2V (Ku et al., 2024), and FLATTEN (Cong et al., 2023), utilizes DDIM inversion for noise initialization to preserve the main structure of the reference video.\"\n\nStyle 2 Related Works: \"Similarly, FLATTEN Cong et al. (2023) utilizes optical flow to identify keypoints and injects their features to maintain motion fidelity. AnyV2V Ku et al. (2024) also leverages spatial, temporal, and CNN features gathered during inversion. While these approaches excel at retaining reference video information, they inherently require an additional stage for inversion, thereby increasing the overall inference cost and computational overhead.\"\n\nThe authors need to fix the in-text citations style (preferably style 1).", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "S1) The framework is simple that it does not require any architecture changes or inversion overhead. By simply concatenating tokens for any desired editing behavior in finetuning, the framework enables new combinations and hybrid tasks. This highlights the originality.\n\nS2) This paper recognizes and operationalizes the fundamentally different natures of video editing tasks, then abstracts them into a unified, learnable token-based framework. Its treats task diversity as token diversity. The motivation is justified.\n\nS3) Smooth transitions in paper writing.", "weaknesses": "W1) Inversion-free approaches like FlowEdit (ICCV 2025) or other direct injection or projection-based methods (where editing is possible without explicit reference-to-noise inversion or heavy architectural mods) are not discussed. The omission is notable, especially as the field moves rapidly toward more general and efficient editing without inversion bottlenecks. Adding the discussion inside would help contexturalize the problem. A more complete landscape would compare: 1) Inversion-based, 2) Adapter-based, 3) Inversion-free, and 4) Unified token-based models.\n\nW2) The paper did not mention which exact video diffusion model was tested in the context. It only mentioned finetuning on a general DiT-based video diffusion model pretrained with flow matching. It remains unclear for us to verify if this can be applied to general cases. Providing the details of the experiment would have enhanced the reproducibility.", "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": "ServXaYb4s", "reviewer": "Reviewer_CmCf", "summary": "The paper proposes UNIC, a simple, adapter-free framework that unifies diverse video-editing tasks, such as ID insert/delete/swap, stylization, first-frame propagation, and re-camera control within one model by concatenating three token types (noisy video latent, reference-video tokens, and multi-modal condition tokens) into a single sequence processed by a DiT with full attention. UNIC features two key designs: Condition Bias (task-type embeddings) and Task-aware RoPE (per-task positional indexing), which mitigate task ambiguity and alignment conflicts. On a six-task benchmark, UNIC reports competitive or superior results to task specialists, shows emergent task composition, and includes ablations and efficiency analyses (e.g., step-cache).", "questions": "1. In appendix E.3, \"All finetuning experiments start from a pre-trained model with 1B parameters and 28 sequential standard Diffusion Transformer (DiT) blocks.\" What is this base pretrained model?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. UNIC reformulates many video editing tasks as tokenized conditions concatenated with noisy and reference tokens.\n2. UNIC supports six representative tasks and demonstrates emergent compositions (e.g. stylization + re-camera).", "weaknesses": "1. I think the paper could benefit from reorganizing its structure for improved presentation. One of the most important parts of training video editing models is in its data construction pipeline, since paired before-and-after editing data is often difficult to obtain. Right now the main text barely has any description of training data, and all of descriptions are in the appendix.\n2. Following weakness 1, there are no statistics about the quantity of the training data in the main text. In Appendix D, there are statistics for some tasks, but not all tasks.\n3. While it is clear that both condition bias and task-aware RoPE benefit model performance, it seems that combining these two designs leads to weaker performance on the style transfer task, and the results of ArtFID and CFSD are even worse than not having the two designs at all. Can the author provide more intuition on this issue?", "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": "9BOZUP445l", "reviewer": "Reviewer_4wrd", "summary": "Whereas prior video editing methods often require inefficient video inversion or the design and training of complex control modules, this paper proposes an in-context approach based on a recently introduced DiT-based video generation model. The method concatenates multiple conditional signals and performs full attention over them, enabling a single unified model to handle diverse conditional editing tasks. To further support such a unified model, the authors introduce condition bias and task-aware RoPE, techniques that help distinguish different conditioning signals and improve performance. They also propose a sequential training scheme to make unified training feasible.", "questions": "- In L473–474, what does “temporal modeling” refer to? Task-aware RoPE seems to improve reference alignment (as Table 6 suggests), whereas “temporal modeling” sounds more like a measure of video quality, making its use here somewhat unclear.\n- In L102, what is meant by “index collisions”?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Unified model for diverse video editing tasks:** The paper models various video editing tasks under a single formulation and demonstrates that learning a unified video editing model is feasible. The two new techniques introduced specifically for unified training are effective and constitute an additional strength.\n- **Timely extension to video:** As development is progressing from image models toward video models, the proposed work arrives at an opportune time.\n- **Clear and intuitive writing:** The paper is easy to follow, and the motivations behind the methods are clear and intuitive.", "weaknesses": "- **Slightly limited novelty:** Although the need to distinguish task-specific conditional signals in unified training is well-motivated and the proposed techniques appear appropriate, the overall design feels like a natural extension from a single signal to multiple signals. Moreover, the proposed methods do not seem tightly tailored to video editing, which makes the contribution feel somewhat less novel.\n\n- **Lack of detailed explanation for method and experiments:**\n    The sequential training scheme appears crucial to performance based on the presented results, yet it is not discussed in the method section. In addition, the paper does not specify which TTV base model is used during fine-tuning.    \n    Furthermore, in Table 6, the performance differences between D2–D4 and the baseline (D1) appear marginal depending on the metric, making it difficult to appreciate the improvements. Additional qualitative comparisons would help clarify the effectiveness of the two proposed components.", "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)