OpenNovelty.org

MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Video generation; Diffusion 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.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces MAGREF, a framework for any-reference video generation that synthesizes videos conditioned on multiple reference subjects and textual prompts. It resides in the 'Attention-Based Subject Disentanglement' leaf, which contains six papers addressing identity consistency and subject confusion through attention mechanisms. This leaf is one of three under 'Multi-Subject Identity Preservation and Disentanglement', indicating a moderately populated research direction focused on preventing feature entanglement when multiple subjects appear in generated videos.

The taxonomy reveals neighboring leaves employing alternative strategies: 'Embedding and Spatial Control for Multi-Subject Synthesis' (five papers) uses subject embeddings and LoRAs, while 'Hierarchical and Cross-Modal Identity Grounding' (five papers) leverages hierarchical structures to link subjects with references. MAGREF's attention-based approach contrasts with these embedding-centric methods, positioning it within a cluster that prioritizes dynamic feature separation over static identity encodings. The broader 'Subject-Consistent Video Synthesis from Reference Images' category (eight papers across three leaves) addresses related appearance preservation challenges but without the multi-subject disentanglement focus that defines MAGREF's core problem space.

Among thirty candidates examined, the framework-level contribution shows one refutable candidate from ten examined, suggesting some overlap with prior unified approaches to multi-subject video generation. The masked guidance mechanism (zero refutable from ten candidates) and the four-stage data pipeline (zero refutable from ten candidates) appear more distinctive within this limited search scope. The statistics indicate that while the overall framework concept has precedent, the specific technical mechanisms for subject disentanglement and training data construction may offer incremental novelty relative to the examined literature.

Based on top-thirty semantic matches, MAGREF appears to build on established attention-based disentanglement paradigms while introducing specific masking and channel concatenation strategies. The analysis covers a focused subset of the field; broader searches or domain-specific venues might reveal additional overlapping work in multi-subject video synthesis or reference-conditioned generation pipelines not captured here.

Taxonomy

Core-task Taxonomy Papers
44
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Any-reference video generation with multiple subject conditioning. The field addresses the challenge of synthesizing videos that faithfully preserve the identities and appearances of multiple subjects drawn from reference images, while maintaining temporal coherence and narrative control. The taxonomy reveals several complementary research directions: Multi-Subject Identity Preservation and Disentanglement focuses on cleanly separating and encoding distinct subject features to avoid identity leakage, often through attention-based mechanisms or specialized encoders. Subject-Consistent Video Synthesis from Reference Images emphasizes fidelity to input appearance cues across frames, leveraging works like Animate Anyone[4] and Videomage[5] that condition generation on reference imagery. Temporal and Motion Control branches explore how to guide dynamics and pose sequences, while Multi-Scene and Narrative Video Generation tackles longer-form storytelling with methods such as Dreamstory[13] and Cinema[15]. Training-Free and Tuning-Free Approaches seek efficiency by avoiding per-subject optimization, and Video Editing and Insertion address localized modifications. Multi-Stage and High-Fidelity Synthesis Pipelines combine refinement steps for quality, whereas View Synthesis and Free Viewpoint Video handle geometric transformations and novel camera angles. A particularly active line of work centers on attention-based subject disentanglement, where methods like Fastcomposer[2] and Disenstudio[7] use cross-attention or layered feature extraction to keep multiple identities distinct during generation. MAGREF[0] sits squarely within this cluster, employing masked attention mechanisms to prevent feature entanglement when conditioning on several reference subjects simultaneously. Compared to Multi-subject Open-set[1], which also tackles open-set multi-subject scenarios, MAGREF[0] emphasizes fine-grained attention control to maintain per-subject consistency. Meanwhile, Refdrop[8] explores dropout-based strategies for reference conditioning, offering a complementary perspective on how to balance subject fidelity with generative flexibility. The central trade-off across these branches remains how to scale identity preservation to many subjects without sacrificing temporal smoothness or narrative coherence, an open question that continues to drive innovation in training-free architectures and multi-stage pipelines.

Claimed Contributions

MAGREF framework for any-reference video generation

The authors propose MAGREF, a framework that enables video synthesis conditioned on arbitrary types and combinations of reference subjects along with textual prompts. It addresses challenges of identity inconsistency, subject entanglement, and copy-paste artifacts in multi-subject video generation.

10 retrieved papers
Can Refute
Masked guidance with subject disentanglement mechanism

The method uses region-aware masking with pixel-wise channel concatenation to preserve appearance features, and injects semantic values from text conditions into corresponding visual regions to prevent subject confusion across multiple references.

10 retrieved papers
Four-stage data pipeline for training pairs

The authors develop a data construction pipeline that creates diverse training pairs to reduce copy-paste artifacts in the generated videos.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

MAGREF framework for any-reference video generation

The authors propose MAGREF, a framework that enables video synthesis conditioned on arbitrary types and combinations of reference subjects along with textual prompts. It addresses challenges of identity inconsistency, subject entanglement, and copy-paste artifacts in multi-subject video generation.

Contribution

Masked guidance with subject disentanglement mechanism

The method uses region-aware masking with pixel-wise channel concatenation to preserve appearance features, and injects semantic values from text conditions into corresponding visual regions to prevent subject confusion across multiple references.

Contribution

Four-stage data pipeline for training pairs

The authors develop a data construction pipeline that creates diverse training pairs to reduce copy-paste artifacts in the generated videos.

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