MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement
Authors: Yufan Deng
Yuanyang Yin
Xun Guo
Yizhi Wang
Jacob Zhiyuan Fang
Shenghai Yuan
Yiding Yang
Angtian Wang
Bo Liu
Haibin Huang
Chongyang Ma
Venue: ICLR 2026 Poster (2026)
Keywords: Video generation; Diffusion models
Primary area: generative models

Abstract: We tackle the task of any-reference video generation, which aims to synthesize videos conditioned on arbitrary types and combinations of reference subjects, together with textual prompts. This task faces persistent challenges, including identity inconsistency, entanglement among multiple reference subjects, and copy-paste artifacts. To address these issues, we introduce MAGREF, a unified and effective framework for any-reference video generation. Our approach incorporates masked guidance and a subject disentanglement mechanism, enabling flexible synthesis conditioned on diverse reference images and textual prompts. Specifically, masked guidance employs a region-aware masking mechanism combined with pixel-wise channel concatenation to preserve appearance features of multiple subjects along the channel dimension. This design preserves identity consistency and maintains the capabilities of the pre-trained backbone, without requiring any architectural changes. To mitigate subject confusion, we introduce a subject disentanglement mechanism which injects the semantic values of each subject derived from the text condition into its corresponding visual region. Additionally, we establish a four-stage data pipeline to construct diverse training pairs, effectively alleviating copy-paste artifacts. Extensive experiments on a comprehensive benchmark demonstrate that MAGREF consistently outperforms existing state-of-the-art approaches, paving the way for scalable, controllable, and high-fidelity any-reference video synthesis. The code and video demos are available in the supplementary materials.

Published as a conference paper at ICLR 2026
MAGREF: MASKED GUIDANCE FOR ANY-REFERENCE VIDEO GENERATION WITH SUBJECT DISENTANGLE- MENT
Yufan Dengl.2, Yuanyang Yin", Xun Guo", Yizhi Wang", Jacob Zhiyuan Fang", Shenghai Yuan?, Yiding Yang?, Angtian Wang', Bo Liu Haibin Huang?, Chongyang Ma² !Peking University Shenzhen Graduate School 2ByteDance
(a) Qualitative Results
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(b) Human Preference Study
Quantituivee Comparison
Figure 1: We present MAGREF, flexible video generation framework that supports arbitrary combinations of subjects including humans, animals, clothing, accessories, and environments within single generation process, while maintaining visual consistency and faithfully following textual instructions. (a) Qualitative results across diverse subjects and scenes, with reference images provided in the top-left corner. More qualitative cases provided in Figures [0 (b) User study comparing MAGREF with existing models. (c) Quantitative comparison for the multi-subject evaluation set.
ABSTRACT
We tackle the task any-reference video generation, which aims to synthesize videos conditioned on arbitrary types and combinations of reference subjects, to- gether with textual prompts. This task faces persistent challenges, including identity
Thhis work done when Yufan Deng ltw.:/uffan@@@suuuuuuuu.o.
intern ByteDance.

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Published as a conference paper at ICLR 2026
inconsistency, entanglement among multiple reference subjects, and copy-paste artifacts. To address these issues, we introduce MAGREF, a unified and effective framework for any-reference video generation. Our approach incorporates masked guidance and a subject disentanglement mechanism, enabling flexible synthesis conditioned diverse reference images and textual prompts. Specifically, masked guidance employs a region-aware masking mechanism combined with pixel-wise channel concatenation to preserve appearance features of multiple subjects along the channel dimension. This design preserves identity consistency and maintains the capabilittes of the pre-trained backbone, without requiring any architectural changes. To mitigate subject confusion, we introduce subject disentanglement mechanism which injects the semantic values of each subject derived from the text condition into its corresponding visual region. Additionally, we establish a four-stage data pipeline to construct diverse training pairs, effectively alleviating copy-paste artifacts. Extensive experiments on comprehensive benchmark demon- strate that MAGREF consistently outperforms existing state-of-the-ar art approaches, paving the way for scalable, controllable, and high-fidelity any-reference video synthesis. The code and video demos are available in the supplementary materials.
INTRODUCTION
Recent advances in diffusion models Ho et al. (2020b); Song et al. (2020); Peebles & Xie (2023) have substantially enhanced the capability of generating realistic and temporally coherent videos, conditioned on text prompt or single reference image. These breakthroughs have attracted increasing attention from both academia and industry Blattmann et al. (2023a); [Runway] (202)) ) Pika (2025); OpenAA (2024), fueling surge of interest in controllable video synthesis. Beyond text or image driven generation, there is a growing demand for leveraging multiple reference subject to provide fine-grained control over appearance and identity. This paradigm shift has sparked increasing exploration of any-reference video generation, which aims to integrate diverse visual cues into coherent, personalized, and high-fidelity video sequences.
However, conditioning video generation on both textual descriptions and multiple reference images greatly enlarges the condition space, leading intricate interactions among arbitrary combinations of subjects, such as humans, animals, clothing, objects, and environments. This complexity makes it difficult reliably preserve subject identities across frames, to disentangle multiple subjects without confusion, and to avoid copy-paste artifacts when integrating diverse visual cues. In particular, these complexities can be distilled into the following major challenges: (1) identity inconsistency, where appearance details such as facial structure or accessories fail to remain coherent; (2) entanglement across multiple reference subjects, where identities from different reference images are mistakenly blended or confused; and (3) copy-paste paste artifacts, which degrade visual realism and scene integrity. Recent works Yuan et al. (024)) Zhang et al. (2025); Wei et al. (2025) have shown progress in preserving single identity, but they often rely external identity modules and single- image references, limiting scalability in real-world applications. Other approaches Zhong et al.] (2025); Jiang et al. (2025): ; Liu et al. (2025) simplify the conditioning process by concatenating visual tokens along the token dimension, yet these text-to-video frameworks require large-scale datasets and struggle with identity preservation and generalization. Fei et al. (2025) explores an alternative image-to-videe design by concatenating references along the channel dimension with temporal masks, but still falls short in addressing the above challenges in a unified and effective manner.
To overcome these limitations, we propose MAGREF (Masked Guidance for Any- Any-Reference Video Generation with Subject Disentanglementt, which tackles them in three parts. (1) Masked guidance for consistent multi-subject identity preservation. we condition the model on references at the pixel level via a pixel-wise channel concatenation that preserves fine-grained appearance details, and region-aware masking mechanism that composes a reference canvas with spatial support for each subject, enabling precise conditioning across arbitrary subject categories (humans, animals, clothing, objects, environments) within unified architecture without structural changes. (2) Subject disentanglement to mitigate cross-subject confusion. We introduce a subject disentanglement mechansim that explicitly injects semantic values of subject tokens into their corresponding visual regions, thereby enforcing identity separation and reducing cross- reference confusion in any -effreeeee video generation. (3) A systematic four-stage data pipeline to alleviate copy-paste artifacts. We
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design efficient data pipeline that integrates general filtering and caption, object processing, face processing, and cross- pair construction into a unified system, yielding diverse training pairs while suppressing copy-paste artifacts. Together, these components facilitate scalable, controllable, and high-fidelity any-reference video synthesis, enabling the creation of highly realistic videos.
Overall, the key contributions of MAGREF are follows:
We propose a unified masked guidance design that leverages a region aware masking mechanism and pixel-wise channel concatenation inject references at the channel level. This preserves fine-grained appearance cues and enables precise subject conditioning across arbitrary categories, with minimal architectural modifications.
We develop a subject disentanglement mechanism that injects the semantic values from text condition into their corresponding visual regions, enforcing clear separation among identities and mitigating cross-reference confusion without additional identity extraction modules.
We establish a systematic four-stage data pipeline that constructs diverse and cross training pairs, effectively suppressing copy-past artifacts and improving robustness. Extensive empirical evalua- tions show that MAGREF delivers high-que nigh-quality, multi-subject consistent video synthesis, surpassing all existing approaches and achieving ofate-of-the-arr results across several benchmarks.
RELATED WORK
Video generation models. Recent advancements in video generation often rely Variational Autoencoders (VAEs) Li al. (2024); Kingmal 2013);] Van Den Oord et al. (2017) to compress raw
video data into a low-dimensional latent space. Within this compressed latent space, large-scale
generative pre-training is conducted using either diffusion-based methods Ho et al. (2020a)))))) Song
al. (202T) or auto-regressive approaches Yu et al. (2023a): Chen al. (2020); Ren al. (0025).
Leveraging the scalability of Transformer models Vaswani (2017); Peebles & Xie (2023). these
methods have demonstrated steady performance improvements Brooks et al. (2024); Yang et al.
(2024): [Blattaann al. (2023b). This advancement significantly expands the possibilities for content generation and inspires follow-up research on text-to-vides video Guo et al. (2024b); Ronneberger et al. (2015); Yin et al. (2024): Yang et al. (2024); Kong et al. (2024); Wan et al. (2025 Yu et al. (2023b) and image-to-ve to-ge--t-viiden Chen et al. (2023a); Guo et al. (2024a); : Ye et al. (2023); Chen et al. (2023b); (enng
al. (2024); Xing al. (2024) Zhang al. 2023 Blattmann et al. (023b) generation models.
Subject-driven visual generation. Generating identity-consisent images and videos from refer- ence inputs requires accurately capturing subject-specific features. Existing methods can broadly divided into -based and training-fre -fren approaches. Tuning- based solutions Zhou et al. (2024a); Wei et al. (2024); Chen et al. (2025): | Wu et al. (2024) typically rely on efficient fine-tuning strategies, such LoRA Hu et al. (2022) DreamBooth Ruiz et al. (2023). to embed identity information into pre-trained models, but they require re- re-tuning for each new identity, limiting scalability. In contrast, training-free approaches Zhou et al. (2024b); Wang
(2024b) adopt feed-forward inference without per-identity fine-tuning, often enhancing cross-attention or self-attention to better preserve identity consistency.
Recent works have explored various strategies for subject-driven video generation. Some meth- ods focus on identity preservation, such as ConsisID Yuan et al. (2024), which maintains facial consistency via frequency decomposition. Others, like ConceptMaster Huang et al.] (2025) and Alchemy Chen et al. (2025). leverage CLIP Radford et (202T) encoders together with Q- Former Li et al. (2023) to fuse visual-tex embeddings for multi-concept customization. Another line of work Deng et a (2025): Hu et al. (2025) introduces Multimodal Large Language Models (MLLMs), e.g., Qwen2-VL Wang al. (2024a) and LLaVA Liu et al. (2023), to enhance prompt-reference interactions. Building on Wan2.1 Wan et al. (2025), methods such as ConcatID Zhong et al. 2025).,
VACE Jiang et al. (2025), Phantom Liu al. (2025). and SkyReels-A2 Fei et al. (2025) further explore reference conditioning, either by concatenating image latents with noisy latents or injecting reference features conditional inputs to guide the diffusion process.
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XL
Depatdingg STOTeO
Reforoe Iagge LOBES
VAB Enooder
Foer
VAB
Decede:
Region-Ausr
Dynamic Mask
CLIP
Text Tokens
Visual Tokens
shirt pink background.
A woman denim
15
Cress Attenloo
Worth Valao
Add
Channel Concatenaion
Nanilla Mechasiam
Mask Mechonlon
VAL-
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Figure 2: (a) Overview of MAGREF. We introduce region-aware masking mechanism to encode multiple references and concatenate them with noise latents. subject disentanglement that links each reference to its textual label to avoid cross-subject entanglement. Compared with (b) Vanilla masking mechanism, which concatenates references along the frame dimension, (c) Region- aware masking mechanism merges references into a composite image, encodes with VAE, and applies a downsampled binary mask indicate subject regions, thereby better preserving first-frame consistency in 12V models.
METHOD
Given a set of reference images and a corresponding text prompt, our objective is to generate videos that preserve the consistent appearance of the specified subjects. The preliminary background on video diffusion models is provided in Appendix A] We then present our masked guidance and subject disentanglement mechanism, followed by a detailed explanation of the four-stage data curation pipeline, which decomposes video-text data and constructs diverse reference pairs.
3.1 VIDEO GENERATION VIA MASKED GUIDANCE
We propose MAGREF, novel framework for coherent any-reference video generation from diverse reference images (see Figure 2). Unlike single-subject scenarios, the any-reference setting requires the model automatically identify and align subjects with unknown number and distribution. To tackle this challenge, masked guidance mechanism introduces region-aware masking mechanism combined with pixel-wise channel concatenation, which injects information from multiple reference images. This design enables the model to better leverage the preservation capability of the pretrained video backbone and effectively extend the any-reference setting.
Region-aware masking mechanism. To accurately incorporate multi multi-subject information while re- maining consistent with the 12V modeling paradigm, we introduce a region- aware masking mechanism that concatenates images and simultancously generates the corresponding region masks. Specifically, given a set of reference images { > all images are first placed onto a blank canvas at distinct spatial locations {Pk ( / k=1 This creates a composite image Icomp- where each pixel's value determined by the source image occupying its location. This process is formulated as:
Compp (i,j)
Σ Tuli ((i--,,j
yk.j xx) ((.))
where R3 is the rectangular region occupied by image Is on the canvas, and 1 is the indicator function. The composite image Tump is treated as a single reference frame, allowing the model to inherit the native magde-to-video generation capability.
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Testual Frompt
Compusite Iopat Image
Similarity Woman
Similarity
Ouiput Video
Two individuuls taiiig lepether setiing holding umartphone
MAGRET
with his hand extended fieward, exparing the photo. He dressed in light gray blaser over white shirt. The besikk him has long wearing white top....
MAGREF a SD
Figure 3: Cosine similarity visualization between composite reference input image and textual labels. MAGREF achieves more accurate alignment of the Man and the Woman in the multi-subject composite image with the corresponding text prompts. In contrast, removing Subject Disentanglement (SD) results in entangled and ambiguous associations.
In parallel, binary mask is constructed to explicitly indicate the spatial regions corresponding to each subject:
(iij) = (KJ) n.
This mask provides precise spatial prior of each subject in the reference frame, guiding the model to enforce strong subject-level consistency. To further improve robustness, we randomly shuffle subject locations during training to mitigate potential positional bias.
Pixel-wise channel concatenation. Achieving coherent and identity any-reference video generation requires precise identity-aware cues for each subject. Prior methods either inject VAE representations of reference images along the temporal dimension Jiang al. (2025) or concatenate visual tokens after patchification Zhong et al. (2025). However, these approaches require the model to relearn identity consistency from scratch, particularly when handling varying numbers of references, which turn demands large amounts of diverse domain data and ultimately limits generalization, leading to inconsistencies with the input images in anyrreference settings. In MAGREF, rather than concatenating references along the token dimension and relying solely on self attenttenoo Hu et al. (2025); Liu et al. (2025), we introduce region-awar masking mechanism with pixel wise channel concatenation, which preserves subject-specific appearance features and ensures faithful identity consistency.
Specifically, Teomp E IxC, x x is first padded with zeros along the temporal axis to match the dimensionality of video frames, resulting in Tcomp TRTx' x xXx The padded composite is then processed by the VAE encoder F.(.) to obtain the latent feature map:
F comp E(Tcomp) E TXCXHXW
(3)
where T. C, H, and W denote the number of frames, channels, height, and width, respectively. Meanwhile, the binary mask M is downsampled to match the spatial resolution of Feomp and replicated along the channel dimension, yielding Mregion T'xC.,, HHW This ensures that the reference image representation is temporally aligned with the video frames, facilitating seamless integration of reference features across the entire video sequence. The raw video frames are then processed through the same VAE encoder E(-), producing latent representation. Gaussian noise is added to these latents, resulting in Z E R2 XXXXXX
We concatenate the noised video latents Z, the reference image representation Feomps and the feature masks Megion along the channel dimension to construct the final input Finput:
Pinpar Concat (Z, Foomps Mregion)
B (xC+C)))x)
where Concat denotes channel-wise concatenation. The resulting composite input Finput is then fed into the subsequent modules of the framework to enable coherent and identity preetitii any- reference video generation.
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GoundingDINO SAME
0.0
Videos
Some Cut
Score Fillering
Vidao Captoonig
Tag Extraction
Object Extraction
Object Filtring
Stage I: General filtering and captioning.
Stage 2: Object processing and filtering.
/oby
InsightFare
Gementivve
Face Desection
Quality Filtering
Score Ranking
Faoe Selection
Augmeniation
job Stage
590
Stage 3: Face processing and filtering.
Cross-pair data construction.
Figure 4: A systematic four-stage data pipeline for collecting high-quality training samples.
3.2 SUBJECT DISENTANGLEMENT
While masked guidance provides explicit feature regions for each subject and facilitates clear visual separation, aligning multiple subjects with their corresponding textual descriptions remains highly challenging. Unlike single-ID preservation, multi-subject generation requires much stronger coupling between reference images and textual conditions: otherwise, interference and entanglement across subjects are likely to occur. To address this issue, we extend the region- masking mechanism by explicitly associating each reference subject with its corresponding textual information.
Specifically, Subject Disentanglement begins by parsing the text condition extract set of word labels that correspond to the reference subject, denoted as {w, / For each word, we get its corresponding value embeddings V {vr } E R (i = 1,...... KK) from the cross-attention layers. To spatially anchor these semantic concepts in the visual domain, we construct mask for each subject Maub = (M 1 to guide the injection of the corresponding reference image value
embeddings into their designated regions, where M is defined as:
M (i (KJ)ER, E [0,1]
k = K.
The subject-specific information is then directly injected into the latent representation of the first frame 2g BRIxxxHx in each layer and updated as
= = 50 +
(M 2 v.),
(6)
where denotes Hadamard (element-wiss wise)) product with broadcasting to align tensor shapes. This targeted injection operation establishes a tight alignment between the designated image regions and the associated text tokens from the very beginning of the diffusion process. As result, effectively mitigates attribute leakage and prevents interference across different subjects during video generation (see Figure
3.3 POUR-STAGE DATA CURATION
We design a systematic data curation pipeline that processes training videos, generates textual labels. and extracts reference entities including faces, objects, and backgrounds, tailored for the any-reference video generation task. As illustrated in Figure 4 the pipeline comprises four stages that progressively filter, annotate, and construct references for subsequent model training.
In Stage raw videos are segmented using scene-change detection, and clips with low quality or minimal motion are discarded. The remaining clips are captioned using Qwen2.5- VL VLiBii et al. (2025) with focus on motion-related content. In Stage 2, objects are identified from captions, localized with DIN et al. (2024), and segmented into clean reference images using SAM2 Ravi et al. (2024). Stage 3 involves face detection with Insigh[Face]] where faces are assigned identities, filtered by pose, and ranked based on quality, A fixed number of high-quality faces are selected to form the
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A man with migged heard, werting a leather jacker riding vintage mottrryclle along dosnn hightny. His expression
facusell syes slightly against wind.
Ours
Condts ID
Halluo
Phanom
King 3.6
Skyreas
PiKaz.
vocy indour acune fataring ouple conkers usitng vareyy The seial beidde her, buts atrerseaads. his am aorad ber shoudden, posteee awand sceen.
saken apparre
Kiing 1.6
Phantom
VADE
Pika 2.11
v.du2.0
Figure 5: Comparison of our model with state-of of-the-art models on single-ID (top) and multi-subject (bottom) generation. MAGREF demonstrates superior performance over other models.
reference set. Stage 4 leverages state-of-the art image generation model to generate augmented versions of both face and object references, introducing variations pose, appearance, and context. Background images also augmented to enrich the reference set. The final training sample, after all four stages, is formally defined as:
Ohj R, = V1. Oi,, (1!tace, 1! (1 Obj 1 ) .... (L
Bg 1.40
where VÚ denotes the video clip, C2 is the caption, (1Eaee _F are the original and transformed face references, (TO0) yoob', represent the object-variant pairs, and 1.t denotes the background reference.
More details of the pipeline are provided in the Appendix B
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Evaluation settings. For the evaluation benchmark, we select a subset from prior benchmarks Yuan al. (2024) ; |ei al. (2025); Yuan al. (2025), with the remaining cases curated ensure diversity in subjects and scenarios. Finally, we construct set of 120 reference-tex pairs, evenly split between single-ID and multi-subject settings. Single-ID tests use one reference face image, while multi- subject tests cover diverse scenarios, including flexible combinations of two-human, three-human, compositions. Fach case includes no more than three reference images and natural language prompt. Detailed information is provided in the Appendix C.I
and camaa--ohjec--bakeeeee
For evaluation metrics, we consider both single-ID and multi-subject settings to comprehensively as- sess model performance. For single-ID evaluation, we use four metrics: (1) ID-Sim, cosine similarity between face embeddings, evaluating identity consistency Deng et al. (2019): 9); (2) Aesthetic Score, reflecting human perceptual preferences via a predictor trained on high-quality images christoph schuhmann (2024); (3) Motion Smoothness, measuring temporal coherence and motion quality Wu
al. (2023) (4) GmeScore, retrieval-based vision-language alignment metric for semantic
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Table 1: Quantitative comparison on single-ID evaluation. Best in bold, second best underlined.
Model
Venue
ID-Sim
Aesthetic
Motion
GmeScore
Total Score
ConsisID Yuan 2024
0,406
0,418
0.798
0.720
0.586
Echo Wet et al. 20233
0,455
0.399
0.782
0.684
0.580
FantasyID Zhang ct al.
20125
0.304
0.456
0.854
0.726
0.585
Concat-ID Zhong cl al. 20225
0.417
0.441
0.820
0.737
0,604
Open-source
HunyuanCusionn Cusomm Hu A1. 2025
0,592
0.497
0.848
0.697
0.659
SkyReels-A2 eei
0,511 1
0.443
0.842
0.618
0,604
Phantom Liu et
22025
0,492
0,504
0.952
0.722
0.668
ViCIJ al.
21265
0.577
0.524
0.949
0.696
0.687
Hailuo Hailuo 2025
0.537
0.527
0.941
0.714
0,680
Pika Pika 2025
0,301
0.519
0.851
0.738
0.602
Proprietary
Vidu Vidu 2025
0.340
0.476
0.919
0.677
0.603
Kling 1.6 K.mgg 2025
0.359
0.516
0.846
0.672
0.598
MAGREF
Ours
0.595
0.516
0.956
0.710
0.694
Table Quantitative comparison on multi-subject evaluation.
Model
Venue
ID-Sim
Subj-Sim
Bg-Sim
Aesthetic
Motion
GmeScore
Total Score
Skyreels-A2
0.274
0.464
0.507
0.371
0.884
0.659
0.527
Phantom
Open-source
0.481
0.364
0.460
0.458
0.976
0.713
0.575
VACE
0.345
0.463
0.615
0.467
0.968
0.680
0.590
Pika2.1
0,239
0.347
0.596
0.477
0.851
0.676
0.531
Vidu2.0
Proprictary
0,308
0.312
0.617
0.425
0.876
0.680
0.536
Kling1.6
0.387
0.411
0.571
0.458
0.864
0.655
0.558
MAGREF
Ours
0.542
0.496
0.622
0.478
0.945
0.681
0.627
sistency Zhang et al. (2024). For multi-subject evaluation, we introduce two additional metrics: (5) Subj-Sim, assessing consistency across subjects using regions extracted with GroundingDINO Liu ct al. (2024) and SAM2 Ravi et al. (2024): (6) Bg-Sim, evaluating background consistency by an inpainting model Podell et al. (2023). Finally, average all metrics to obtain the Total Score. Complete details are provided in the Appendix C.3
Training details. We train our model using the FusedAdam optimizer, configured with B1 0.9, 32 = 0.999, and weight decay of 0.01. The learning rate is initialized at x 10 and follows cosine annealing schedule with periodic restarts. To stabilize training and prevent exploding gradients. we apply gradient clipping with maximum norm of 1.0, which benefits the optimization process. All experiments are conducted using NVIDIA H100 80GB GPUs and PyTorch. The training loss follows the standard diffusion loss formulation, outlined in Wan et al. (0255).
4.2
MAIN RESULTS
Qualitative results. Figure I[a) presents representative examples generated by MAGREF, with ad- ditional qualitative
and applications provided in the Figures 14] Appendix F.T MAGREF demonstrates the ability to support arbitrary combinations of subjects including humans, animals, clothing, accessories, and environments within single generation process, while maintaining strong consistency and faithful alignment with textual instructions.
We further compare MAGREF with state-of-thh-ar methods in Figure 5 MAGREF demonstrates superior identity preservation, stronger adherence to textual instructions, and better generalization in out-of-domain scenarios both single-ID and multi-subject settings, compared to both open-source and commercial models, thus providing strong evidence of our approach's 's efficacy in addressing the challenges of any-reference video generation.
Quantitative results. We conduct systematic evaluation of MAGREF against both open-source and proprietary models (for details on the evaluation models, see the Appendix C.2). Since some existing methods only support single-ID inputs, report single-IID results in Table T and multi-
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Table 3: Ablation on training paradigm and masking strategies.
Method
ID-Sim
Subj-Sim
Bg-Sim
Aesthetic
Motion
GmeScure
Total Scure
Training from T2V backhone with Vanilla Masking
0.428
0.403
0.468
0.450
0.891
0.657
0.550
0,458
0.431
0.492
0.437
0.876
0.653
0.558
12V with Regional-awart Mask (Ours)
0.504
0.452
0,526
0.452
0.906
0.679
0.587
Table 4: Ablation of the entire MAGREF pipeline.
Method
ID-Sim
Subj-Sim
Bg-Sim
Aesthetic
Motion
GmeScore
Total Score
region- aware masking mechanism
0.470
0.452
0.530
0.443
0.872
0.652
0.570
wlo gross-puir data process strategy
0.462
0.447
0,524
0.464
0.892
0.656
0.574
w/o subject disentanglement mechanism
0.493
0.417
0.518
0.452
0.919
0.679
0.580
Ours
0.542
0.496
0.622
0.478
0,945
0.681
0.627
subject results in Table 2 Across both settings, MAGREF consistently achieves the best performance in subject consistency (ID-Sim and Subj-Sim) and ranks highest in terms of overall Total Score. These results highlight the effectiveness of MAGREF in preserving subject identity and maintaining visual quality, while also demonstrating superior robustness across diverse evaluation scenarios.
4.3 ABLATION STUDIES
Region-aware masking mechanism. Table shows that training from T2V backbone or using vanilla masking from an 12V backbone results reduced identity and subject consistency. In contrast, introducing the region-aware masking mechanism significantly improves performance and achieves the highest overall score. We validate all methods on a small-scale dataset with equal training steps and use the same training resources to ensure fairness. The ablation of region-aware masking mechanism on the overall pipeline Table further confirm this finding. In addition, We provide qualitative comparisons of different masking and concatenation mechanisms in Appendix D.I and Figure 6
Cross-pair data processing strategy. As reported in Table removing the cross-pair data pro- cessing strategy leads to noticeable decline in overall performance, with particularly significant reduction in the model's ability to minimize copy-paste artifacts. This observation underscores the importance of the cross-pair augmentation strategy in addressing such artifacts. This enhanced generalization not only helps mitigating the occurrence of poste artifacts but also contributes to more robust video synthesis process. The inclusion of cross-pale pross-pair data processing improves the model's 's ability to handle diverse and complex scenarios, resulting in more coherent and visually consistent outputs. Overall, this strategy plays crucial role in improving the synthesis quality, ensuring that the generated videos are more realistic and aligned with the given textual descriptions.
Subject disentanglement mechanism. Table 4 shows that removing the subject disentanglement mechanism leads to a noticeable decrease in both ID-Sim and Subj-Sim, weakening subject con- sistency. This confirms that explicitly binding each subject its corresponding textual condition effectively reduces interference, improving multi-subject video generation quality. Additionally, Figure 3 visualizes the cosine similarity between composed reference images and their corresponding textual labels. The results show that MAGREF with the subject disentanglement mechanism aligns the Man and Woman precisely, while removing it causes entangled associations, emphasizing the im- portance of disentanglement for clarity in multi- generation. More qualitative cases analyzing the subject disentanglement mechanism can be found in the Appendix D.3
4.4
USER STUDY
We conduct user study utilizing pairwise voting strategy assess the performance of different video generation models. Each questionnaire consists of 60 questions, where participants are asked to compare two videos and determine which one is superior if the two equally good. The videos presented in each pair are randomly sampled from different models, ensuring a diverse range of comparisons. For each question, participants evaluate the videos based three key criteria:

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Ablatina on Different Musking Mechanisms
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Figure 6: Ablation study on masking and concatenation schemes. Left: Comparison of different masking mechanisms. Our proposed masking mechanism maintains identity consistency and visual coherence under varying reference conditions (Top row). In contrast, the vanilla masking mechanism, which concatenates reference images along the channel dimension, results in temporal inconsistency and identity drift (The second row: our ee-ipleementatinn Bottom row: SkyReels- A2 Fei al. (2025)) Right: Comparison of different concatenation mechanisms. Pixel-wise channel concatenation preserves fine-grained reference features, improving consistency with reference images. In contrast, token-wise concatenation dilutes identity-specific cues and weakens identity preservation (The second row: our re-implementation; Bottom row: Phantom Liu et al. (2055) ))
identity preservation, visual quality, and alignment with the provided text description. guarantee comprchensive assessment, recruit 30 experienced participants, ensuring wide spectrum of subjective perspectives. This approach allows us to capture nuanced opinions and provide a robust evaluation. As illustrated in Figure the results demonstrate that method significantly outperforms existing state-of-the-art models, thereby validating the effectiveness of our approach in addressing the challenges of the any-reference video generation task.
CONCLUSION
In this work, we introduce MAGREF, a unified framework for any-reference video generation that combines pixel-wise channel concatenation with region-aware masking mechanism, enabling the synthesis of coherent videos with multiple distinct subjects, MAGREF also incorporates subject disentanglement mechanism and a four-stage data pipeline to enhance performance and reduce common artifacts. Extensive experiments show that MAGREF outperforms state-of-the methods, excelling in any-reference scenarios with strong temporal consistency and identity preservation. Future work will extend MAGREF to support unified understanding and generation using mulli-moda. large language models, enabling synchronized synthesis of video, audio, and text.
Limitations and future work. While MAGREF delivers promising perormance in any-reference video generation, it has key limitations address in future work: only supports image and text inputs, without integrating audio, motion or 3D spatial modalities to expand its application scope; has not fully explored the impact of reference image quantity on performance, with limited references restricting its capture of subject variations and contextual details; its non-MLLM -bssed T5 text encoder struggles capture complex text semantics, undermining text-visual alignment and video fidelity; and it lacks support for controllable long video generation, its short-clip- optimized framework cannot maintain temporal consistency, subject identity and content coherence over extended durations.
In summary, future work will address these limitations by extending MAGREF to support multi- modal generation using advanced MLLMs, enabling synchronized synthesis of video, audio, and text, as well as the generation of long videos with controlled subject consistency and motion dynamics. By incorporating additional input modalities, optimizing the use of reference images, and improving textual understanding, we aim to further enhance the flexibility, scalability, and realism the video generation process.
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ETHICS STATEMENT
Our work adheres to the ICLR Code of Ethics. The proposed framework does not involve human or animal subjects, nor does it raise privacy, security, legal compliance concerns. We follow proper licensing and usage guidelines. We are not aware of any potential harmful societal impacts, conflicts of interest, bias issues introduced by this work. This research conducted in line with standard practices for research integrity and reproducibility.
REPRODUCIBILITY STATEMENT
We are commitee to ensuring the reproducibility of our results. To this end, we will release the full codebase, pretrained models, and detailed instructions for running experiments upon publication. The dataset processing pipeline and evaluation metrics described in detail in the main paper and appendix, and additional implementation details are included in the supplementary materials. We have also fixed random seeds and specified hardware/software environments to facilitate consistent reproduction of our results.
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MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement
Appendix
Preliminaries
16
Data Curation Pipeline
17
B.
General Filtering and Captioning.
17
B.2
Object Processing and Filtering.
17
B.3
Face Processing and Filtering,
18
Cross-Pair Data Construction.
19
Experiment Settings
20
Evaluation Benchmark
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Evaluation Models
20
Detailed Evaluation Metrics
21
D Additional Ablation Results
22
D.I Ablation Details on Masked Guidance
22
D.2 Ablation Details on Reference Image Scalability
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D.3 Ablation on Subject Disentanglement Mechanism
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More Qualitative Results
24
More Results of MAGREF
24
Failure Cases Visualization
25
Additional Statement
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The Use of Large Language Model
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A PRELIMINARIES
In this section, we summarize the basic formulation of our generative framework. The core idea of video diffusion and fow-matching models is to construct continuous transformation that maps gaussian noise into structured video data, conditioned on external signals such as text.
Flow Matching, Instead of relying on stochastic diffusion process, we employ flow matching Lip man al. (2022), which defines a deterministic trajectory between the noise distribution and the target video distribution. Let a E R. x denote a clean video, and 2n ~ NO 7) random noise sample. For a timestep [0, 1]. the interpolated state is written as:
2t = Lxn + (1 1) znn-
(
Velocity Field. The dynamics of the trajectory are governed by its velocity, obtained by differenti- ating with respect
dx
0: dt
20 Fy-
(9)
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Training Objective. The model, parameterized by 8, is trained to approximate the true velocity with predictor u( y, t; 0), where y represents the conditioning signal. The loss function is defined
( 20 t. te ( y, 1, Us 11
(10)
During inference, an ODE solver integrates the estimated velocity field to evolve noise samples toward realistic videos, guided by the conditioning input y. To reduce the computational burden of high-dimensional video data, we employ a pretrained variational autoencoder (VAE) to map raw video sequences X E IR Fxxxxxxxxxx xxx into compact latent representation 2n R/xexxxx Here, (F, C, H. W) and (f. h, w) denote the frame, channel, and spatial dimensions before and after compression, respectively. This transformation preserves essential semanties while substantially reducing memory and computation cost.
B DATA CURATION PIPELINE
We present the detailed data curation pipeline used in our work, as illustrated in Figure 4 The pipeline designed to construct a high-quality, large-scale training dataset in a systematic manner. consists of four stages: (1) general filtering and captioning, (2) object processing and filtering, (3) face processing and filtering, and (4) cross-pair construction. Each stage progressively refines the raw data, ensuring both quality and diversity.
B. GENERAL FILTERING AND CAPTIONING.
In the first stage, we aim to ensure clean and diverse video corpus by segmenting raw training videos into high-quality clips. To achieve this, we apply scene change detection to break each video into multiple clips, denoted as V1, V22 ..... Vn, Unlike traditional text-to-video approaches, which focus on broad video content, our goal is to generate subject-centric video captions. To achieve this, we employ Qwen2.5-VL.
al. 2025), large vision-language model, to describe the appearance and changes of the subject, while preserving key contextual elements of the video such as the environment and camera movements. The model generates captions C, that emphasize the subject's actions and visual transformations over time, while ensuring that environmental details and movement cues remain intact. We then evaluate the aesthetic quality and motion amplitude of each clip and discard any that do not meet the required standards, ensuring high-quee high-quality and subject-relevan training data.
B.2 OBJECT PROCESSING AND FILTERING.
The second stage focuses on extracting and refining object-centric representations. For each filtered video clip V. we first extract candidate object labels from the generated captions C2 using Qwen2.5- Bai et al. (2025). These labels represent the objects present in the video, such as "cat" or "dog." which serve as initial object candidates for further processing. Next, we apply GroundingDIINO Liu al. (204)) grounding model that localizes each object by predicting bounding boxes in the frames. Let the bounding box for an object & in video clip V; be represented as:
Bi_ = ( h Vi.k; Wi,k hx.k);
(11)
where (Zi,k: )). denotes the lep-left corner of the bounding box, and (wi,,, hi.k) represent its width and height, respectively.
Once we have the bounding boxes, we use SAM2 Ravi al. (2024) segment these regions into object reference images 1 The segmentation masks are generated by SAM2 and represent the precise boundaries of the detected objects. The corresponding reference image for an object clip V, is denoted as:
Ohj SAM2(V, Bi,k),
(12)
where SAM2 is the segmentation model applied to the bounding box B to generate an accurate segmentation mask for the object.
To ensure the reliability and accuracy of the object references, we further refine the segmentation masks by applying morphological operations, such erosion and dilation, smooth the object
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boundaries and remove small noise. Let the refined mask be denoted as Mr,
Mi Saaphhology(M....
(13)
where M,, is the original segmentation mask, and Mr is the refined version obtained by applying the erosion and dilation operations. These operations help to eliminate small artifacts and improve the continuity of the object boundaries.
Additionally, we remove objects that are either too small or have abnormally irregular shapes, which unlikely provide useful features for training. This is done by using size threshold m to discard small objects:
JObj if area(M,, < Omin: remove i,k
(14)
where area(MM,.. is the pixel area of the segmentation mask M,, and min is the minimum size threshold. Objects that fall below this size are considered noise and discarded from the object references.
For objects that overlap with human faces in the scene, we apply Non-Maximum Suppression (NMS) to eliminate redundant object masks that may cause false positives or conflicts with human subjects. The overlap between two masks, My (object mask) and Ma.ec (face mask), is calculated using the Intersection--vee--Uun (IoU) metric:
MM, O Mi,eace IoU(M Ms.Faee
(15)
M k U M,rcel
where Mi, and Maae are the object and face masks, respectively. If the IoU between the object mask and the human face mask exceeds a threshold of 0.25, we consider it significant overlap and remove the object reference image 7.0bj.
if IOU Mr.Face) 0.25, remove 1.
(16)
These steps ensure that the object references 1 are high-quality, relevant, and suitable for use in subsequent stages of the pipeline. By removing small, noisy, or irrelevant objects and refining the segmentation results, we can better capture the objects that are most important for understanding the video content, thereby improving the overall quality of the training dataset.
B.3 FACE PROCESSING AND FILTERING.
Human faces are critical aspect of identity preservation in video data, especially for tasks involving consistent subject tracking or identity recognition. Therefore, we dedicate a separate stage for face-specific processing ensure that face-related features are accurately extracted and maintained throughout the video clips.
We begin by using InsightFace state-of-the-art face recognition and analysis library, detect faces across all frames in each video clip V,, as well as in adjacent segments. For each frame, InsightFace detects multiple potential faces and extracts features that represent the face's unique identity. Each face is then embedded into a nigh-diimessioal feature space, providing a robust representation of the identity. This embedding is used for identity assignment, allowing us differentiate between different faces within the same or across clips.
In order to increase the reliability of the face detection process, we also estimate pose attributes for each detected face. Specifically, calculate the yaw, pitch, and roll of each face, which represent its orientation in space. These pose attributes are important for distinguishing faces that may be tilted or viewed from uncommon angles. To improve robustness, discard faces with extreme pose values or those detected at low quality (e.g., blurred or occluded faces), as they would introduce noise into the dataset.
For each unique identity, we rank the detected faces by two criteria: detection confidence and pose quality. The detection confidence is measure of how certain the model is that the detected face is indeed a face, while the pose quality reflects how well the face's orientation aligns with standard frontal views. Faces with high detection confidence and optimal pose qualities are prioritized.
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To ensure balanced representation of identities across all frames, uniformly sample 10 faces for each unique identity, avoiding the risk over-representing any specific pose. These 10 selected faces chosen to span the diversity of poses and qualities available within the clip, ensuring that capture wide range of possible face orientations while maintaining identity consistency.
The selected faces then assembled into set of human reference faces 1, which represents the set of cropped face reference images for each video clip V. Specifically, 7f contains the highest-scoring, frontal faces detected within the clip, ensuring that the final set of faces used for training is both diverse and consistent across different poses and quality levels.
We denote the of all human reference faces across all video clips in the dataset IFace, which represents the collection of all selected face reference images from the entire corpus:
IFace = U Face 1.2
FFace
(17)
where Face represents the k-th highest-scoring frontal face from the i-th video clip. Each i corresponds iik a different video clip, and the set {4,T1 "............................... Face) contains the highest-scoring frontal faces detected in clip i, ensuring high confidence in identity consistency. This set ensures that the model can consistently reference and learn from human faces that are not only representative the subject but also exhibit optimal frontal orientation for better identity recognítion and pose estimation.
Formally, each curated training sample after Stage 3 processing is defined as:
R, V2, C₁ 1,000, 1
Ob 4,2
(18)
where Vi denotes the ground-truth video clip, C, represents the text caption, LTnee denotes the cropped face reference, and 12 corresponds to the object references. This structured representation ensures that each training sample is aligned with the relevant video content, captions, faces, and objects, enabling robust model training.
B.4 CROSS-PAIE DATA CONSTRUCTION.
In Stage 4, we focus addressing the issue of copy-paste artifacts, which often arise when the model generates videos where the poses and orientations of subjects remain overly consistent with those in the reference images. This stage aims to enhance the diversity of the multi-subject dataset and improve the model's ability to generalize across varied visual scenarios. By leveraging an image generation model, we generate transformed variants of both face and object references, reducing the risk of overfitting to specific, static
pairings.
For each face reference /face and each object reference r obtained in Stages 2 and 3, the image
generation model produces augmented counterparts 1.aae'' and 1.90 with variations in pose, appear- ance, and context. These transformations are designed to simulate a broader range of real-world conditions, ensuring that the model is not simply learning from unaltered, static pairings of faces, objects, and backgrounds. These variations help mitigate the potential for the model to learn artifacts related to repetitive patterns, such as those caused by directly copying objects onto backgrounds. In addition to the face and object transformations, background images are also augmented to further enrich the reference set.
The goal of this augmentation process is to create a dataset where the foreground (face and object) and background are less rigidly linked, preventing the model from relying too heavily repetitive artifacts.
object- srreet-hackgroung pairings and thereby reducing the risk of
Formally, each training sample after Stage 4 is defined as:
R₂ = Vi. Ct. (I, FFace' ), (T 1 ),
(TOR) I ), 1887.
(19)
where (J.aaee [.Fce' are the original and transformed face references. (1 Ohj 100 represent the object- variant pairs, and 1.t denotes the background reference. The training sample R; includes both the original and transformed face and object references, along with a corresponding background, resulting in more diverse and less repetitive training dataset.
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By integrating these augmented references into the training process, we ensure that the model not only learns from high-quality, subject-centric data but also gains the ability to generalize effectively across wide variety of visual contexts. This strategy significantly reduces the occurrence of copy-paste artifacts, leading to more natural and realistic interactions in generated videos. Together, these four stages form systematic pipeline that transforms raw, noisy video data into high-quality, semantically aligned training samples, essential for scalable and controllable any-reference video generation.
C EXPERIMENT SETTINGS
C.1 EVALUATION BENCHMARK
Existing benchmarks for any-reference video generation have notable limitations, particularly in assessing the flexibility and robustness of generative models across wide range of complex scenarios. To address this gap, we propose systematic and task-specific benchmark designed to comprehen- sively evaluate our video generation framework in both single-ID and multi-subjecc settings. This benchmark consists of 120 subject-text pairs, divided into two primary categories: single-ID and multi-subject. The single-ID group includes 60 test cases, each involving a single ID reference image, while the multi-subject group encompasses 60 cases with varying complexities, such two-person, three-persse and mixed scenarios, including caimaa--bbjeet-bacgrrouud compositions.
A subset of the benchmark is adapted from existing datasets such ConsisID Yuan et al. (022)) OpenS2V Yuan et al. (2025), and A2-Bench Fei et al. (2025), ensuring a consistent foundation for comparison. The remaining cases carefully curated to guarantee comprehensive coverage across diverse subject types, background settings, and interaction dynamics. Each test case consists of no more than three reference images, accompanied by natural language prompt designed to maintain high aesthetic quality and semantic alignment. This controlled structure ensures consistent difficulty across the benchmark, allowing for detailed and rigorous evaluation of the generative model's performance.
The diversity of the benchmark is integral to its design, incorporating varying subject appearances, prompt lengths, and compositional arrangements. This enables a fine-grained evaluation of the model's 's ability to synthesize coherent and diverse videos, accounting for a broad spectrum of visual and semantic complexity. By incorporating real-world elements such as varying background contexts and dynamic subject interactions, the benchmark provides robust testbed for evaluating the model's capacity to generate realistic, high-fidelity videos under challenging conditions. This approach not only ensures a comprehensive assessment of the model's generative capabilities but also highlights its potential for generalization across variety of complex scenarios.
C.2 EVALUATION MODELS
We evaluate a representative set of mainstream proprietary and open-source models for the any- reference video generation task, comprising 4 proprictary and 8 open-source models. The proprictary models include Hailuo (2025). Pika (2025). Vidu (2025), and Kling (2025). Among these, Hailuo is evaluated in the ID) setting, whereas Pika, Vidu, and Kling are evaluated on both single-II) and multi-subject tasks. For open-source baselines, ConsisID Yuan et al. (022)), Echo Video Wei et al. (2025), FantasyID Zhang et al. (2025). Concat-ID Zhong et (2025) and HunyuanCustom Hu et al. (2025) are used for single-ID evaluation. SkyReels-A2 Fei et al. (025)), Phantom Liu et al. (2025). and VACE Diang et al. (2025) are evaluated on both single-ID and multi-ubbjcct tasks. The detailed evaluation protocols and configuration specifics are provided below.
For Hailuo, we use the official S2V function of Hailuo-S2V -2V-01 with default settings, generating a 5 5-second video (141 frames) at resolution of 1280 x 720 and a frame rate of 25 fps. For Pika, we utilize the official Pika 2.1 with default parameter settings, producing a 5-second video (121 frames) at a resolution of 1920 x 1080 and a frame rate of 24 fps, which allows for a comprehensive assessment of the model's performance in generating high-resolution videos. For Vidu, we use Vidu 2.0's character-to-videe function with default settings in turbo mode, generating a 4-second clip (65 frames) at 16 fps with a spatial resolution of 704 x 396 and automatic motion amplitude. For Kling, employ the official Kling 1.6 with default settings, producing 5-second video (153 frames) at resolution of 1280 x 720 and a frame rate of 30 fps, enabling an in-depth evaluation of the model's performance across varying visual and semantic contexts.
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For open-source single-id evaluation, the official code and models for ConsisID, Video, FantasyID, Concat-ID, and HunyuanCustom, maintaining the original settings. For ConsisID, videos generated spatial resolution of 720 480 with frame rate of 8 fps, yielding duration of 6 seconds (49 frames). Echo Video generates 3-second videos (49 frames) at resolution of 848 x 480 and frame rate of 16 fps. Fantasy generates 6-second videos (49 frames) at 720 x 480 and fps. Concat-ID generates 5-second videos (81 frames) at 832 x 480 and 16 fps for the Adn-AdaLN version. Lastly, HunyuanCustom generates 5-second videos (129 frames) a resolution of 1280 X 720 and 25 fps. Each setup ensures consistency in video generation while varying resolution and frame rate across the models for effective comparison.
For open-s multi-subject evaluation, we use the official code and models for SkyReels- Phantom, and VACE, maintaining the original settings. For SkyReels-A2, we employ the A2-Wan2.1 14B-Preview model, generating 5-second videos (81 frames) at resolution of 832 x 480 and frame rate of 16 fps. In Phantom, we use the Phantom- Saattom-aanllee Wan-14B model to generate 5-second videos. For VACE, we the VACE-Wan2. 1-14B to generate 5-second videos (81 frames) at 1080 720 and 16 fps. Each setup ensures consistent video length, frame rate, and model performance across the three models, allowing for effective comparison.
C.3 DETAILED EVALUATION METRICS.
For evaluation, we consider both single-IID and multi-subject settings to comprehensively assess model performance. The following sections provide a detailed explanation of the six evaluation metrics used in this study, focusing on their practical relevance in any-reference video generation tasks.
ID-Sim (Identity Similarity) The ID-Sim metric measures the consistency of the human's 's identity across video frames. This is done by calculating the cosine similarity between face embeddings extracted from each frame of the video using pretrained face recognition model, ArcFace Deng al. 2019). To ensure a representative evaluation, we select frames at regular intervals (every 16th frame) and exclude frames where no face is detected. The cosine similarity sim B) between two face embeddings A and B is computed as:
sim (A B) = A.B ||]
(20)
where A B is the dot product of the embeddings and ||-|]] and || are their respective Euclidean norms. A higher cosine similarity value indicates better identity preservation across frames, meaning that the model maintains the subject's appearance and characteristics consistently throughout the video.
Aesthetic Score The Aesthetic Score christophschhhmaan (2024) evaluates the visual quality of the generated video based on human perceptual preferences. This metric is derived from learned aesthetic prediction model, which trained on large dataset of high-quality images capturing subjective factors such as color harmony, sharpness, and overall composition. The aesthetic score Sweshetie for the entire video is calculated as the average of the frame-wise aesthetic scores Suesthetic (T)) (Tt), where (T+) represents the learned function that outputs the visual appeal of each frame. The overall score then given by:
Saeshetic
S(A),
(21)
N
where N is the total number of frames in the video.
Motion Smoothness To evaluate the fluidity of motion in the generated video, we employ the Motion Smoothness metric Wu et al. (2023). This metric measures the temporal coherence of movement between consecutive frames, which is essential for ensuring that motion transitions smoothly and naturally, without abrupt changes or artifacts. It is crucial for maintaining the realism and continuity of dynamic actions within the video.
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GmeScore The GmeScore Zhang et al. (2024) is used to evaluate the semantic alignment between the generated video and the input text. Traditional models such as CLIP and BLIP are often used for text-to-image text-to-video relevance but limited by short token lengths (usually 77 tokens), which makes them unsuitable for handling long-form text prompts typical DiT-hased video generation models. GmeScore is based vision-language alignment model fine-tuned on Qwen2-VL and is capable of processing longer and more complex text descriptions.
Subj-Sim (Subject Similarity) The Subj-Sin Simj-iim metric assesses the consistency of the subject across video frames. For each video, we sample frames at equal intervals and extract the regions corresponding to the subject in both the generated video and the ground-truth (GT) images using segmentation models like Grounding/DINO Liu et al. (2024) and SAM2 Ravi et al. (2024). The embeddings for both the GT subject and the video frame subject are obtained using the DINO model.
The cosine similarity sim(S,, Sg)) between the embeddings of the subject regions S; from the video frames and the ground-truti subject Sgi is calculated for each frame i:
S Sgt
s Sg:)
(22)
|||
where S; and Sy are the embeddings of the subject in the i-th video frame and the GT subject, respectively. The average similarity score Subj is then computed by averaging the cosine similarities over all sampled frames:
N Σ
Sabb
sim Sg)),
(23)
N
where N is the total number of frames sampled. Higher similarity values indicate better consistency in the subject's appearance across frames.
Bg-Sim (Background Similarity) The Bg-Sim metric evaluates the consistency of the back across video frames. Similar Subj-Sim Sim, calculate the similarity between the background of the inpainted video frames and the ground-trutt truth background by sampling frames at equal intervals. The inpainting model Podell al. (2023) is used reconstruct missing altered regions of the background in the video. The DINO model used to extract embeddings for both the inpainted background and the ground-truth background. The cosine similarity Rog between the embeddings of the inpainted background B; and the ground-truth background Bg for each frame i is calculated as:
B₁ Bg
Rig
(24)
BIll B
where B, and By: are the embeddings of the inpainted background and the ground truth background for the i-th frame, respectively. The average background similarity Sbg is computed by averaging the cosine similarities over all sampled frames:
N
Sog
Rog:
(25)
where is the total number frames sampled. Higher background similarity values indicate that the background remains consistent and realistic across frames.
D ADDITIONAL ABLATION RESULTS
D.1 ABLATION DETAILS ON MASKED GUIDANCE
In this section, we conduct detailed evaluation of the two central components of masked guidance, the region-aware masking mechanism and the pixel-wise channel concatenation mechanism, and provide an in-depth analysis of their effectiveness.
Region-aware masking mechanism. The region aware masking mechanism is designed to commodate a variable number of reference images in a spatially adaptive and content-aware manner. Rather than relying fixed concatenation strategy, it selectively modulates the visible regions
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of each reference image during training, enabling the model to dynamically allocate attention to semantically meaningful areas. This fine-gres grained mechanism aligns more closely with the natural variability of multi-subject and multi-object scenes, where different references may occupy distinct spatial regions or contribute unevenly across time.
To illustrate its effect, compare two masking strategies: fine-grained region-aware masking mechanism (top of Figure (6) and a coarse-grained vanilla masking mechanism, which follows the design of SkyReels-A2 Fei et al. (2025) (bottom of 6i. The vanilla approach concatenates reference images directly along the channel dimension, ignoring spatial locality. As shown in Figure b(b) and (c), this naive strategy often causes frame-level inconsistencies and identity drift, particularly during long video synthesis. Even after discarding the initial warm-up frames, subsequent generations frequently degrade visual quality, leading to unstable motion and the gradual loss of subject fidelity. These issues indicate that coarse channel concatenation combined with uniform masking introduces strong interference, which undermines temporal coherence and hinders the stable inheritance of subject identity.
In contrast, the region-aware masking mechanism explicitly regulates the contribution of each reference image across both space and time. By masking irrelevant or redundant regions and preserving only task-relevant cues, the model avoids channel-level entanglement and significantly reduces cross-subject interference. This allows the generator to better exploit fine-graiie aeee-graaedd visual information, while simultaneously maintaining consistency with the 12V training paradigm. As a result, the generated videos exhibit sharper details, smoother motion dynamics, and more faithful preservation of subject identity, even under long-horizon generation settings. Overall, this ablation study highlights that spatially adaptive region-aware masking erucial for stabilizing training, reducing identity drift, and improving the perceptual quality of any-reference video generation.
Pixel-wise channel concatenation mechanism. We perform ablation experiments to compare two strategies for integrating reference images: the proposed pixel-wise channel concatenation mechanism and the token-wise concatenation mechanism commonly adopted in prior work Hu et al.) (2025); ; Liu et al. (2025). As shown in Figure our pixel-wise channel concatenation consistently demonstrates superior identity preservation, especially in reconstructing fine-graines facial structures and subtle appearance cues. By embedding reference images directly into spatially aligned feature channels, the model receives strong supervision signals that are tightly coupled with the spatial layout of the generated frames.
In contrast, the token-wise concatenation approach treats reference images as additional tokens that are injected into the transformer input sequence. In this setting, the model relies entirely on self-attention layers to extract and propagate identity-related information. Such indirect encoding weakens the supervision of identity cues during training, since identity information across tokens and more prone to diffusion. As illustrated in Figures[6[e) and (f), this often results in inconsistencies in subject appearance, such as blurred facial features, unstable textures, or even identity drift over longer generations.
scattered
These shortcomings become even more pronounced when the model encounters out-of-domain reference images, where the distributional gap between training data and unseen references chal- lenges its generalization ability. Under token-wisc wise concatenation, the model struggles to robustly transfer identity cues from such references, frequently producing distorted or mismatched identities. In contrast, pixel-wise concatenation leverages spatially grounded and semantically rich features that anchor identity information more effectively, thereby reducing failure cases in out-of-domain scenarios. Overall, these results highlight the advantages of our design: by directly embedding reference cues in pixel-aligned representations, our approach significantly improves both in-domain generalization in any-reference video generation.
fidelity and
D.2 ABLATION DETAILS ON REFERENCE IMAGE SCALABILITY
To further evaluate the scalability of our approach with respect to reference images, we perform a comprehensive qualitative experiment. As shown in Figure 7 we provide qualitative examples that demonstrate how the model performs with reference images of varying scales. These examples illustrate how the subject and style information is preserved and transferred effectively across different sizes, even when the reference images are significantly reduced. The results clearly indicate that the
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Canvas
DIe diaaes IN Deva Somple 123 Dovn Sample 1.2 Donn Samppe 1 11
Different Scale
Prompt
A man gently clutching a bouquet of vibrant flowers, his eyes radiating serene contentment he glances at the camera. His slightly upturned lips convey a sense of calm joy, accompanied by
ie Wimnnn tum is
faint twinkle his eye. The scene is set in lush garden, brimming with colorful blooms and verdant
Figure 7: Qualitative examples showing how the model handles reference images of different scales. The model maintains consistency in the generated output, even when the reference image is significantly reduced.
model maintains consistency in the generated output, regardless of the reference image's size. This observation suggests that approach handle up to 8 reference images simultaneously without a significant loss of quality or detail. Furthermore, we find that the overall effect does not exhibit substantial variations with changes in the size of the reference images. These findings demonstrate the robustness and flexibility of our method in managing range of image scales, making it scalable for various applications.
D.3 ABLATION ON SUBIECT DISENTANGLEMENT MECHANISM
qualitative results of the ablation study the Subject Disentanglement (SD) mechanism. When SD is removed, we observe severe entanglement between different subjects, such blending of facial features, inconsistent appearances across frames, and failure maintain distinct identities in multi-subject scenarios. For example, in the first row, the absence of SD causes the doctor and patient to gradually lose their unique characteristics, leading to identity drift. Similarly, in the second case, the two individuals in the selfie scene show visual confusion, with faces and attributes becoming entangled over time. The third case demonstrates that human-aning model without SD not only fails to preserve subject identities but also hallucinates an additional dog, indicating entanglement and instability in multi-subject scenarios. By contrast, our full model with SD effectively disentangles subjects, maintains identity fidelity, and produces temporally coherent results across diverse scenarios. These results highlight the importance the SD mechanism for handling complex any-reference generation tasks.
Figure 9 presents
interactions, the
E MORE QUALITATIVE RESULTS
E.1 MORE RESULTS OF MAGREF
We provide additional qualitative results of our method in Figures 10 14
which further demonstrate
the effectiveness of MAGREF in synthesizing coherent videos from paired text prompts and reference images. Our model consistently preserves the distinct visual attributes of the provided references while faithfully following the input text conditions.
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Reference images
Prompt
A stands outdoors with mountainous background. He white tank top, light blue jeans, and dark His hands are in his pockets, and he has relaxed smile. The seene is illuminated by strong, radiant sunlight, with beams of light radiating from the sky, creating warm, glowing stmosphere.
Man'
Light>
A man stands outdoors with mountaineus background. He wears white tank top, light blue jeans, and a dark cup. His bands are in his pockets, and he has relaxed smile. The top cloth beige Burbery hoodie a drawstring hood. illuminated strong, radiant sunlight, with beams light radiating from the sky, creating glowing atmosphere..
Man>
Clothess>
<Ligght>
man stands outdoors with mountainous background. wears a white tank top. light blue jeans, and dark cap. His hands are in his pockets, and relaxed smile. The top cloth beige Burberry boodie with drawstring hood. The scene is illuminated by the mesmerizing vibrant green of the aurora borealis.
Atmosphere'
Figure 8: More qualitative experiments exploring global element customization in video gen- eration. Qualitative examples demonstrate that our method can flexibly adapt global attributes like lighting and environmental atmosphere in zero-shot manner. This highlights the potential of the method to control global features such as lighting and atmosphere without sacrificing subject identity.
Figure 10 highlights human human-object compositions involving accessories such as bags, rings, and necklaces. Figure [T extends this to glasses, while Figure 12 shows results with clothing such as blouses, polo shirts, hoodies, jackets, hats, and sweaters. Figure 13 illustrates single-ID cases, highlighting identity preservation across generated frames. Finally, Figure demonstrates multi- subject scenarios, including persons, animals, and scenes.
Together, these results confirm that our model generalizes well across accessories, glasses, clothing, single-ID, and multi-subject composition tasks, accurately capturing interactions between people, objects, and environments while generating contextually appropriate and visually compelling videos.
E.2 FAILURE CASES VISUALIZATION
Although our method demonstrates strong overall performance, certain failure cases still arise in specific scenarios (see 15). One key challenge is the scarcity high-quality data that effectively captures complex subject interactions, which limits the model's ability to generalize, particularly in scenes involving multiple subjects or intricate subject-object dynamics. As a result, the model may struggle maintain subject consistency and coherence when handling such interactions. Additionally, the current foundation models are insufficient in modeling physical laws, leading to unrealistic phenomena in some scenarios. For example, when large-scale motions involved, the model
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doctur, dressed white cost and glasess is seated at desk with a laptop upen in front of him. He appears engaged conversation with patsent who is seated accoss from him wheelchair. The pasient, wearing dark blue sweater over collared smiling wormly and gesturing with indicating discusssion.
MAGREF
MAGREF mio SD
individunle grase, seting person on the left, wearing white shirt, holding smariphonne and appears to be taking selfie. The person on the right, dressed
background
sleoveless lenning against the other person, suggesting close relationaliip.
MAGNEF mHo SD MAGREF
man walking his dog purk.
MAGREF who SD MAGREF
Figure 9: More qualitative comparison for the ablation on Subject Disentanglement (SD). The proposed MAGREF preserves subject identities and prevents entanglement across different scenes. while the variant without SD (MAGREF w/o SD) exhibits identity drift, blending, and loss of consistency when multiple human animal subjects appear simultaneously.
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young woman silling against weathered wall expression. wearing beige blazer black high-nock lop and dark pinstripe punts. She waven hamihag with gold hardware.
A young stands against gray wall, wearing litted pink dress with short sleeves and round neckline. Her hand rests her head. She wears natural-colored woven bag with a brown leather strap over her shoulder.
A man slands outdoors, leaning against a tree trunk. He wears a black hut, sunglasses, and dark blue T-shirt, and he looks off to with a serious expression. A red blue backpack leans against tree next to him.
A man walking barefoot on beach, wearing heige trench coat and blue jeans. is walking towards the ocean, with three orange chairs placed near water. bag brown canvas backpack with multiple pockets and straps.
close-up portrait of young face and upper body. She wearing beige white shirt with button-down collar, and beige cardigan. She is wearing large, dangling earrings with a floral design and green accents.
cluse-up portrail of young woman leaning railing. looking directly the The woman gold ring with emerald stone surrounded by smaller diamonds.
expression.
young woman stands against dark background, her eyes closed as she gently touches one while other her chest. The necklace, featuring green clover-shuped pendants, around neck.
A young woman stands agninst gray speckled wall, smiling while holding an iPad. Her curly hair frumes her face as she looks down tablet. necklace a pink choker silver spikes beart-shoped cutout.
Figure 10: Qualitative results any-reference human and object composition. Each row conditions on two reference images: a human reference on the left and an object reference on the right (bag, ring, necklace). Our model supports arbitrary pairings between humans and accessories, reliably identifies the intended object, even when the object reference is cluttered or contains distractors, and faithfully follows the text prompt, producing effects akin video editing.
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A close-up portrait young with curly red hair, wearing black strupless top with necklace. She holding her hair back with one hand and bas gold bracelet her wrist. The woman is wearing orange-tinted sunglosses with gold frame.
A close-up up portrait of man's face and upper body. He is wearing green jacket with high collar and zipper. His hair is styled neatly, and be has a scrious expression on his face. The man is wearing gold-framed sunglasses with gradicat lenses.
LITTLE
CHT
BLACK
BOOK
close-up portrait young woman's face and upper body. Her bair styled loose waves, wearing large hoop earrings. The looking directly at the camera with neutral expression. She black- ftamedd glasses her face.
A close-up portrait of man's face and upper body. He is wearing a jacket with high collar and zipper. He has serious expression on his face. The sungl aanglasses have sleek, modern design with a gunmetal frame and dark lenses.
young woman green vintage camera background. wearing long-sleeve shirt. She holds bouguet white tulips and smiles warmly. She glasses with tortoiseshell frames.
A man sitting at table with his arm resting on it. smiling with a coafident expression. He is wearing dark blue suit jacket over white shirt with the collar slightly open. The glasses have tortoiseshell pattern with gold logo on temples.
A young woman sitting black leather chair with green plants in the background. She wearing black glasses with a thin gold earring in her left car. The woman resting her chin on hand, looking slightly to side with thoughtffll expression.
A close-up portrait of man's upper body. The man is looking directly at the camera with slight smile. The background is plain white. The man is wearing pair of matte black Holbrook RX glasses.
Qualitative results on any-reference human and glasses composition. Each row conditions on two reference images: a human reference on the left and glasses reference on the right. Our model supports arbitrary pairings between humans and eyewear, reliably identifies the intended glasses even when the reference image is cluttered or contains distractors, and faithfully follows the text prompt, producing effects similar to video editing.
Figure 11:
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young woman sitting on rocks by the sea, wearing mint green sheer blouse with high neckline and long sloeves. The blouse has ruffled collar at the nock. The background screne blue occan under clear sky.
young man standing in studio white background. wearing navy polo shirt white stripes on the shoulders and sleeves. He relaxed pese with his hands packets...
A young woman stands in urban parking garage, wearing neon green sheer blouse with bow at the neckline. She has long dark hair, sunglasses, and latloos en her arms. She wears high- jeans with chain accessory.
A young woman stands against backdrop of flowers, wearing pink hoodie with heart and heartbeat design. She has short bob hairstyle looking off to the side with neutral expression.
young woman stands studio with a white background. She is wearing a lace jacket with long sleeves and floral brooch at center. The jacket light heige intricate patterns. is pulled back, and wears gold hoop earrings.
SE
young standing studio with white background, He wearing gray Burberry sweater with white logo and running horse design. The sweater has round nookline and long sleeves. The is also wearing cark sunglasses and blue jeans.
A young womsn stands ouldoors with a blurred cityscape in the background. Her hands are lucked into the coal pockets, and she looks directly camera with neutral expression. She wears red knitted with large for pom-pom on top.
A close-up up portrait of man's face and upper body. The man is smiling slightly, with his eyes looking directly the camera. The man is wearing cowboy hat with wide brim, brown band, and decorative metal accents.
Figure 12: Qualitative results on any-reference human and clothing composition. Each row conditions two reference images: human reference on the left and clothing reference on the right (e.g., blouse, polo shirt, hoodie, jacket, or sweater). Our model supports arbitrary pairings between humans and garments, reliably identifies the intended clothing item even when the reference cluttered or includes distractors, and faithfully follows the text prompt, producing effects similar to video editing.
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young woman long standing green
girl standing by beach, long gently swaying breeze, wearing flowy, hipe
jogs along besido
curly hair wearing
A large bright studio. She wearing paint splattered apron has her hair tied up bun.
A hiking through danse loresi. He wearing backpack, liking books, and cap, with light jacket protect the wind.
young appears
he wearing green sleeveless top
A man standing outdoors snewy park, wearing colorful winter jacket with floral partem and white knit hat
man sits at rustcc wooden table café, sipping coffee. short black and dressed in casual button-up shirt and joans.
Figure 13: Qualitative evaluation results of our method on test cases involving a single ID. Our model consistently generates videos that maintain the subject's identity while accurately following the input text prompt.
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THEET
TIATES
The video features two individunls, a man and woman, dancing against a bright yellow background. Both are dressed casually; man wears red and black plaid shirt over white t-sbirt, the woman green button-up shirt layerod over white top.
openss screne sctting featuring green individuals scaled on during daytime,, with clear weather and sunlight filtering through the trees. The individuals are dressed casually, with one person wearing white tank top patterned shorts, the other plaid shirt and dark pants.
The video depicts two individuals seated table, engaged in discussion while looking at tablet device. The person on the left is dressed blue scrubs, indicative healthcure professional, and is holding the tablet. The individual right wearing white lab collared shirt and tie, suggesting they are doctor or medical professional.
Three individunls seated bench, engaged with electronic devices. person the left, wearing a green white checkered shirt, is focused smariphone. The individual in the midkle,, dressed in beige sweater, holds tablet and looks intently it. The person the right, clad in peach-colored cardigan and sunglasses hanging around her neck...
A man sitting in the office, a cat sitting on his legs.
sitting the park, walking around his feet.
man feeding a bird the park.
Figure 14: Qualitative results of our method on test cases involving multiple concepts. Such as persons, animals, and scenes. Our model is capable of understanding and encoding multiple subjects based the reference images.
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A man is playing with a beach ball by the sea.
A man is playing soccer on a football field.
Figure 15: Failure cases.
may produce structural breakdowns, such as incorrect object manipulation or unnatural physical behavior. These issues not unique approach but commonly observed across existing methods. Addressing these challenges will require the development of richer datasets that capture more complex subject interactions, along with more advanced foundation models that can better simulate physical behaviors and dynamics.
F ADDITIONAL STATEMENT
F.1
THE USE OF LARGE LANGUAGE MODEL
We leveraged large language models (LLMs), including ChatGPT, to assist with manuseript prepa- ration. Their use was limited to language-related tasks such grammar and spelling correction, stylistic polishing, and word choice refinement to improve clarity and readability. to standardize terminology across sections, check the consistency of figure and table captions with the main text, and streamline reference wording and section cefss-cefsrences. Please note that all scientific ideas, analyses, and conclusions were conceived, verified, and interpreted solely by the authors.
also used LLMs
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Reviews:
[{"review_id": "XWOgzJXNXz", "reviewer": "Reviewer_YBDZ", "summary": "The paper introduces MAGREF, a framework for any-reference video generation that combines region-aware masked guidance with subject disentanglement to support arbitrary combinations of human, object, and environment references. It achieves superior identity preservation and visual quality compared to open-source and proprietary baselines.", "questions": "As seen in weakness", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Technically sound: Masked guidance and pixel-wise concatenation are simple yet effective extensions of I2V backbones.\n\n2. Comprehensive results: The experiments and ablations clearly support the claimed improvements.", "weaknesses": "1. My major concern is that the pixel-wise channel concatenation may limit scalability when the number of reference subjects grows. It is unclear how the model handles more subjects simultaneously. Would temporal or latent-level concatenation yield more flexible conditioning in such cases?\n2. While pixel-wise concatenation effectively preserves subject appearance, it may inherently limit global-level customization. Since it injects spatially grounded features, the model mainly captures concrete subject geometry rather than abstract global styles (e.g., tone, lighting, art style). Temporal or latent-level fusion could offer more flexibility for such style control. Can this framework be extended to global element customization, such as atmosphere, or texture?", "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": "VkFUwNvo6o", "reviewer": "Reviewer_fwvD", "summary": "To solve the issue of any-reference video generation task, this paper proposes a unified framework, MAGREF. The framework contains region-aware masking mechanism and subject disentanglement mechanism. Experiments show the scalable, controllable, and high-fidelity any-reference video synthesis results.", "questions": "See above weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "-  The writing is easy to understand, and the painting is well-drawn.\n-  The proposed region-aware masking method preserves subject identity without backbone changes.\n-  Experimentally, the paper achieves best single-ID and multi-subject score.", "weaknesses": "- 1.\tIn this paper, multi-subjects are introduced into the videos through a blank canvas. Several subjects are directly added to the canvas with their pixels values. Although this function is useful, my major concerns are listed below:\n -      a)\tThe canvas size is limited. How many subjects can be placed on the canvas without harming the model’s generation ability?\n -      b)\tThe positions of these subjects are randomly shuffled during training, in my opinion, the locations of different subjects may contain implicit relationship, such as decide the distance of two subjects in the generated videos. However, this is not discussed in the paper and related ablations are not considered.\n -      c)\tSimilar to the absolute locations, the scale of each subject in the canvas is still missing. Because such model relies on the explicit vision cues to catch the details of the reference subjects, the scale is an important factor that needs to be considered.\n- 2. In subsection “Pixel-wise channel concatenation”, the composited image I_{comp} is encoded by VAE encoder and then concatenated with noised video latents along the channel dimension. I cannot find technically something new that differs from existing methods. Existing methods also apply the pixel-wise image/video with VAE encoder and concatenate the latent with noised video.\n- 3. The paper claims that the methods support arbitrary subject categories in Lines 098-103, but in their methods, it is unclear how the model support such ability.\n- 4. The qualitative comparison in Fig. 5 seems cannot demonstrate the superiority of the proposed methods, such as compared with close-source method Kling1.6 or the open-source method VACE. Besides, why does the multi-subject result of Skyreels method shows show a poor first frame?\n- 5. The experimental results lack more deep analysis to explain why the proposed methods outperform the previous methods in qualitative and quantitative results.\n-6. The failure cases and more analyses should be discussed.", "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": "dFfjxfW0jn", "reviewer": "Reviewer_B9UU", "summary": "This paper proposes MARGREF,  a unified masked guidance design by combining region-aware masking with pixel-wise channel concatenation, to inject reference cues at the channel level. It further proposes a subject disentanglement mechanism that maps text semantics to their corresponding visual regions, cleanly separating identities and mitigating cross-reference confusion without extra identity modules. The model achieved SOTA performance in consistent subject-driven video generation.", "questions": "- What is the value of C_m?\n- What is the base T2V model that MAGREF is fine-tuned on?\n- For the composed reference image, does the resolution matter?\n- For the composed reference image, does the organization matter?\n- In 4.3, what is result in Table 3 / 4 tested on? the score seems different from Table 1 / 2?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper proposed a novel structure to condition video generation on multiple images by combining multiple images into one. It also proposed data-pipeline to collect large-scale\n- The proposed subject disentanglement mechanism is novel and effective in text-prompt alignment for different subjects in the image.\n- The results show that the proposed framework is better than other baselines. Ablation study proves that all the proposed module is meaningful.\n- The paper is well-written and easy to follow.", "weaknesses": "The paper is in general good, some minor points:\n- The computational power is not mentioned e.g. how many GPUs have been used\n- No details about the dataset e.g. source/size and possible privacy problem for human face data", "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": "Skbzkwi6nd", "reviewer": "Reviewer_pDBr", "summary": "This paper proposes MAGREF, a unified framework for any-reference video generation, addressing challenges such as identity inconsistency, subject entanglement, and copy-paste artifacts. The method integrates masked guidance and subject disentanglement mechanisms, along with a four-stage data pipeline. Experiments show that MAGREF achieves superior performance over existing methods, demonstrating its effectiveness for scalable for  subject-driven video synthesis.", "questions": "(1)  The overall spatial resolution for all subjects is fixed. During inference, how are reasonable spatial resolutions allocated to different subjects? If a subject is very large, such as the background, this implies that the spatial resolution for other subjects will be scaled down, which may lead to information loss.\n\n(2) In Equation (6), is $M^k_{\\text{sub}}$  a typographical error? Should it instead be written as $M^i_{\\text{sub}} $?\n\n(3) Is Equation (6) also applied during inference? In addition, can Equation (6) be extended to scenarios involving subject interactions, where the subject masks may overlap?\n\nAddressing my concerns would lead me to reconsider and potentially raise my score.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "The citations are comprehensive, suggesting that the authors are well-versed in the field of subject-driven generation.\n\n\nThe experiments are extensive, encompassing various open-source and commercial baselines, including both single-subject and multi-subject settings. These results clearly demonstrate the effectiveness of MAGREF.", "weaknesses": "If the base image-to-video model does not use channel concatenation to fuse the reference images, the effectiveness of this method would be limited.", "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)