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BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration
Zhaoyang Li, Dongjun Qian, Kai Su, Qishuai Diao, Xiangyang Xia, Chang Liu, Wenfei Yang, Tianzhu Zhang, Zehuan Yuan
TL;DR
Subject-consistent video generation remains difficult when prompts specify complex relationships, temporal logic, and interactions among multiple subjects. BindWeave uses an MLLM-DiT framework to bind text with visual subjects through cross-modal reasoning, and it outperforms existing open-source and commercial models on OpenS2V.
Problem
Existing video generation models struggle to parse complex spatial relationships, temporal logic, and multi-subject interactions while preserving subject identity.
Method
BindWeave uses a pretrained MLLM to ground entities and disentangle roles, attributes, and interactions, producing subject-aware states that condition a Diffusion Transformer.
Results
BindWeave achieves state-of-the-art performance on OpenS2V, outperforming open-source and commercial models across subject consistency, naturalness, and text relevance.
Takeaways & Limitations
The framework supports subject-consistent generation across single-subject and complex multi-entity scenarios with explicit cross-modal integration.
Abstract
from arXiv · showhide
Diffusion Transformer has shown remarkable abilities in generating high-fidelity videos, delivering visually coherent frames and rich details over extended durations. However, existing video generation models still fall short in subject-consistent video generation due to an inherent difficulty in parsing prompts that specify complex spatial relationships, temporal logic, and interactions among multiple subjects. To address this issue, we propose BindWeave, a unified framework that handles a broad range of subject-to-video scenarios from single-subject cases to complex multi-subject scenes with heterogeneous entities. To bind complex prompt semantics to concrete visual subjects, we introduce an MLLM-DiT framework in which a pretrained multimodal large language model performs deep cross-modal reasoning to ground entities and disentangle roles, attributes, and interactions, yielding subject-aware hidden states that condition the diffusion transformer for high-fidelity subject-consistent video generation. Experiments on the OpenS2V benchmark demonstrate that our method achieves superior performance across subject consistency, naturalness, and text relevance in generated videos, outperforming existing open-source and commercial models.
1 INTRODUCTION
BindWeave addresses the difficulty of generating subject-consistent videos from prompts involving multiple subjects, complex spatial relationships, and temporal logic. It uses an MLLM to reason across text and reference images, and achieves state-of-the-art results on OpenS2V.
- Existing subject-to-video methods struggle with complex multi-subject prompts because separate image and text encoders are fused only after independent processing.This limitation can produce identity confusion, misplaced actions, and blended attributes.
- BindWeave replaces shallow multimodal fusion with MLLM-based reasoning that grounds textual commands to visual entities and their interactions.The resulting hidden states encode subject identities and prescribed interactions before conditioning the diffusion generator.
- BindWeave jointly conditions a DiT on MLLM-derived subject-aware states and CLIP reference-image features for subject-consistent video generation.
- BindWeave achieves state-of-the-art performance across subject consistency, temporal naturalness, and text-video alignment against open-source and commercial models.The evaluation uses the OpenS2V benchmark and includes qualitative evidence from generated samples.
2 RELATED WORK
Prior work advances video generation and subject control through diffusion transformers, per-subject optimization, and conditioning modules. These approaches provide useful foundations but involve computational costs and identity-conditioning challenges.
- Diffusion Transformer models have become an important architecture for video generation because of their scaling properties.
- Early subject-consistent methods fine-tune pretrained models for individual subjects, which is computationally expensive and difficult to use for real-time applications.
- More recent methods shift toward end-to-end conditioning networks and adapters that inject subject identity information during generation.
3 METHOD
BindWeave combines multimodal instruction planning with collectively conditioned latent video diffusion. An MLLM binds text to reference subjects, while textual, identity, and appearance signals jointly guide the DiT.
- 3.1 PRELIMINARIES: The generator operates in the latent space of a spatio-temporal VAE and uses a Transformer backbone with spatio-temporal attention and text cross-attention.
- 3.3 INTELLIGENT INSTRUCTION PLANNING VIA MLLM: BindWeave uses a pretrained MLLM to reason over text and reference images, producing hidden states that encode cross-modal semantics and spatio-temporal logic.Textual descriptions are aligned with corresponding image placeholders before MLLM processing.
- 3.3 INTELLIGENT INSTRUCTION PLANNING VIA MLLM: The method combines MLLM-derived relational conditioning with T5 text embeddings to form a joint signal for DiT generation.The joint representation captures explicit commands together with subject interactions and spatio-temporal logic.
- 3.4 COLLECTIVELY CONDITIONED VIDEO DIFFUSION: Reference-image VAE features and binary subject masks are inserted into padded temporal slots before patch embedding, preserving video temporal structure while adding appearance guidance.
- 3.4 COLLECTIVELY CONDITIONED VIDEO DIFFUSION: Within each DiT block, separate attention streams inject relational conditioning and CLIP identity features alongside low-level reference appearance information.The output sums attention from the joint relational and CLIP conditions, while VAE features enter through the input representation.
- 3.5 TRAINING AND INFERENCE: BindWeave is trained with mean squared error between the predicted and ground-truth velocity using data filtered from OpenS2V-5M.The curated training set contains approximately 1 million video-text pairs.
4 EXPERIMENTS
BindWeave is evaluated on OpenS2V-Eval against open-source and commercial baselines using quantitative, qualitative, ablation, user-study, and artifact analyses. Across these evaluations, it shows stronger subject consistency, naturalness, semantic alignment, and visual quality, while combining MLLM and T5 conditioning improves performance over T5 alone.
- 4.2 QUANTITATIVE RESULTS: BindWeave achieves a new state of the art on OpenS2V-Eval’s overall Total Score, with notably stronger NexusScore for subject consistency.The benchmark evaluates 180 videos per method across seven categories with eight automatic metrics; Table 1 reports the quantitative comparison.
- 4.3 QUALITATIVE RESULTS: Across representative single- and multi-subject scenarios, BindWeave provides stronger subject consistency, text alignment, visual quality, and natural motion than competing methods.The comparisons cover single-body, human–entity, single-object, and multi-entity subject-to-video settings.
- 4.4 ABLATION STUDY: T5+Qwen2.5-VL consistently outperforms T5-only across aesthetics, motion, naturalness, and text relevance.The MLLM-only setup was omitted from quantitative analysis because it was unstable during training and failed to converge within the available training budget.
- 4.4 ABLATION STUDY: Qualitative ablations show that MLLM+T5 improves scale grounding, action–object execution, and temporal and textual coherence over T5-only conditioning.These comparisons provide visual support for the quantitative ablation findings.
- 4.5 USER STUDY: In a 20-participant user study, BindWeave performs best in subject consistency while maintaining leading results for prompt following, video quality, and motion quality.Participants scored anonymized samples on a 1–5 scale across four criteria.
- 4.6 COPY–PASTE ARTIFACTS: Conflict–coherence tests evaluate whether reference-image conditioning causes copy–paste artifacts across facial expressions, outfit changes, and pose settings.The supplied passage describes the evaluation design but does not report its outcome.
5 CONCLUSION
The paper concludes that BindWeave enables subject-consistent, text-aligned, and visually compelling videos across single- and multi-entity settings through explicit cross-modal integration. It reports state-of-the-art OpenS2V performance against open-source and commercial methods.
- 5 CONCLUSION: BindWeave delivers consistent, text-aligned, and visually compelling videos across single- and multi-entity settings through explicit cross-modal integration.The framework uses an MLLM to integrate reference-image and textual information for entity, attribute, and relation modeling.
- 5 CONCLUSION: On the OpenS2V benchmark, BindWeave achieves state-of-the-art performance over existing open-source methods and commercial models.The conclusion presents this as evidence of the framework’s strength for subject-to-video generation.
ETHICS STATEMENT
The ethics statement describes the provenance and intended use of the images and qualitative results presented in the paper.
- ETHICS STATEMENT: All paper images are model-generated or sourced from publicly available datasets under their respective licenses.The statement says the images are used solely to demonstrate the technical capabilities of the research.
- ETHICS STATEMENT: Qualitative visual results are provided for academic comparison and research discussion, not commercial use.
REPRODUCIBILITY STATEMENT
The reproducibility statement points to detailed architecture, training, inference, dataset, benchmark, and metric descriptions in the main text and supplementary sections.
- REPRODUCIBILITY STATEMENT: The paper documents network architecture, variable interactions, training and inference parameters, datasets, benchmarks, and evaluation metrics.These details are referenced in Sections 3.2, 3.5, and 4.1 as part of the reproducibility effort.
A.1 APPENDIX OVERVIEW
The appendix contains sections on LLM usage disclosure, model-design justification, and visual comparisons in complex multi-subject scenarios.
- Appendix structure: The appendix covers LLM usage disclosure, model-design justification, and visual comparisons under complex multi-subject settings.These topics are identified as Sections A.2, A.3, and A.4.
A.2 LLM USAGE DISCLOSURE
The authors report using LLMs only for minor grammar checking after drafting, with no role in the research or its outputs.
- LLM usage: LLMs were used only for minor grammar checking and did not contribute to ideation, methods, experiments, results, code, data, or figures.The authors state that all suggested edits were manually reviewed and that no non-public or sensitive information was provided.
A.3 JUSTIFICATION FOR MODEL DESIGN
The model-design appendix motivates combining MLLM and T5 conditioning: MLLM-only variants were unstable, while the combined design improves reasoning and qualitative video behavior over T5-only conditioning.
- Model-design justification: MLLM-only conditioning variants failed to converge within the available training budget, motivating retention of T5 as a stable text-conditioning backbone.The explored alternatives included MLLM+MLP+DiT and MLLM+Q-Former+DiT.
- T5-only versus MLLM+T5: Across challenging multi-reference and multi-subject cases, MLLM+T5 produces coherent spatial arrangements, stable motion, and higher visual quality than T5-only.The comparison includes five-image references and complex interactions; T5-only shows distortions and temporal jitter.
- Model-design justification: The MLLM adds compositional reasoning and cross-entity constraint satisfaction, complementing T5’s dense captions and stable descriptive prior.T5-only can miss cross-entity relations, event ordering, and subject roles, causing layout instability and drift.
- Subject-to-video comparisons: BindWeave avoids copy-paste artifacts by preserving subject consistency alongside natural, temporally coherent motion, whereas baselines can keep the subject static across frames.The cited comparison specifically identifies Phantom-1.3B and VACE-14B as copying the reference cat into the video.
- Subject-to-video comparisons: With single-face references, BindWeave preserves fine-grained identity cues across pose, expression, viewpoint, and illumination changes.With multiple references, it also maintains each subject’s identity, appearance details, relative layout, and coordinated interactions without identity swapping or blending.