# BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration

arXiv: https://arxiv.org/abs/2510.00438 · HTML: https://mlsanity.com/paper/2510.00438/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 36 of its 42 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 30 of 36)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Open-Sora: Democratizing Efficient Video Production for All](https://mlsanity.com/paper/2412.20404/) (2024)
- [Movie Gen: A Cast of Media Foundation Models](https://mlsanity.com/paper/2410.13720/) (2024)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models](https://mlsanity.com/paper/2405.04233/) (2024)
- [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://mlsanity.com/paper/2411.17440/) (2024)
- [Phantom: Subject-consistent video generation via cross-modal alignment](https://mlsanity.com/paper/2502.11079/) (2025)
- [ID-Animator: Zero-Shot Identity-Preserving Human Video Generation](https://mlsanity.com/paper/2404.15275/) (2024)
- [HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation](https://mlsanity.com/paper/2505.04512/) (2025)
- [CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects](https://mlsanity.com/paper/2401.09962/) (2024)
- [SkyReels-A2: Compose Anything in Video Diffusion Transformers](https://mlsanity.com/paper/2504.02436/) (2025)
- [OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation](https://mlsanity.com/paper/2505.20292/) (2025)
- [ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning](https://mlsanity.com/paper/2501.04698/) (2025)

## Built on by (most cited first, 23 of 23)

- [DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation](https://mlsanity.com/paper/2602.12160/) (2026)
- [RefAlign: Representation Alignment for Reference-to-Video Generation](https://mlsanity.com/paper/2603.25743/) (2026)
- [Scaling Zero-Shot Reference-to-Video Generation](https://mlsanity.com/paper/2512.06905/) (2025)
- [ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation](https://mlsanity.com/paper/2606.11670/) (2026)
- [FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization](https://mlsanity.com/paper/2605.15824/) (2026)
- [VideoArgus: Agentic Rubric-Grounded Unified Evaluation for Video Generation and Editing](https://mlsanity.com/paper/2608.05485/) (2026)
- [WildActor: Unconstrained Identity-Preserving Video Generation](https://mlsanity.com/paper/2603.00586/) (2026)
- [AlcheMinT: Fine-grained Temporal Control for Multi-Reference Consistent Video Generation](https://mlsanity.com/paper/2512.10943/) (2025)
- [Omni-Customizer: End-to-End MultiModal Customization for Joint Audio-Video Generation](https://mlsanity.com/paper/2605.17488/) (2026)
- [TIDE: Task-Isolated Diffusion for Unified Video Editing and Generation](https://mlsanity.com/paper/2606.08260/) (2026)
- [Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment](https://mlsanity.com/paper/2607.04311/) (2026)
- [Spectral Heat Flow for Conservative Token Condensation in Vision-Language Models](https://mlsanity.com/paper/2607.10640/) (2026)
- [CustomX: Unified Character, Action, and Scene Customization in Video World Models](https://mlsanity.com/paper/2512.17796/) (2025)
- [MiVE: Multiscale Vision-language features for reference-guided video Editing](https://mlsanity.com/paper/2605.14664/) (2026)
- [DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation](https://mlsanity.com/paper/2606.26058/) (2026)
- [HOMIE: Human-object Centric Video Personalization via Multimodal Intelligent Enhancement](https://mlsanity.com/paper/2607.18217/) (2026)
- [ONE-SHOT: Compositional Human-Environment Video Synthesis via Spatial-Decoupled Motion Injection and Hybrid Context Integration](https://mlsanity.com/paper/2604.01043/) (2026)
- [Generate Your Talking Avatar from Video Reference](https://mlsanity.com/paper/2604.27918/) (2026)
- [Squeezing Capacity from Multimodal Large Language Models for Subject-driven Generation](https://mlsanity.com/paper/2605.26111/) (2026)
- [Spatial-Temporal Decoupled Reference Conditioning for Identity-Preserving Text-to-Video Generation](https://mlsanity.com/paper/2606.02441/) (2026)
- [Vera: Identity-Faithful Human Subject-to-Video Generation](https://mlsanity.com/paper/2607.20247/) (2026)
- [Harnessing Intrinsic Subject-Aware Attention for Controllable Multi-Subject Video Generation](https://mlsanity.com/paper/2609.11507/) (2026)
- [MSR: Multiple Subject Reference for Video Generation](https://mlsanity.com/paper/2609.18393/) (2026)

## Abstract (arXiv)

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.