BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration
Zhaoyang Li 1,2, Dongjun Qian 2, Kai Su 2*, Qishuai Diao 2, Xiangyang Xia 2, Chang Liu 2, Wenfei Yang 1, Tianzhu Zhang 1*, Zehuan Yuan 2
1University of Science and Technology of China 2ByteDance
*Corresponding Author
## 📖 Overview BindWeave is a unified subject-consistent video generation framework for single- and multi-subject prompts, built on an MLLM-DiT architecture that couples a pretrained multimodal large language model with a diffusion transformer. It achieves cross-modal integration via entity grounding and representation alignment, leveraging the MLLM to parse complex prompts and produce subject-aware hidden states that condition the DiT for high-fidelity generation. For more details or tutorials refer to [ByteDance/BindWeave](https://github.com/bytedance/BindWeave) ### OpenS2V-Eval Performance 🏆 BindWeave achieves a solid score of 57.61 on the [OpenS2V-Eval](https://huggingface.co/spaces/BestWishYsh/OpenS2V-Eval) benchmark, highlighting its robust capabilities across multiple evaluation dimensions and demonstrating competitive performance against several leading open-source and commercial systems. | Model | TotalScore↑ | AestheticScore↑ | MotionSmoothness↑ | MotionAmplitude↑ | FaceSim↑ | GmeScore↑ | NexusScore↑ | NaturalScore↑ | |------|----|----|----|----|----|----|----|----| | [BindWeave](https://lzy-dot.github.io/BindWeave/) | 57.61% | 45.55% | 95.90% | 13.91% | 53.71% | 67.79% | 46.84% | 66.85% | | [VACE-14B](https://github.com/ali-vilab/VACE) | 57.55% | 47.21% | 94.97% | 15.02% | 55.09% | 67.27% | 44.08% | 67.04% | | [Phantom-14B](https://github.com/Phantom-video/Phantom) | 56.77% | 46.39% | 96.31% | 33.42% | 51.46% | 70.65% | 37.43% | 69.35% | | [Kling1.6(20250503)](https://app.klingai.com/cn/) | 56.23% | 44.59% | 86.93% | 41.6% | 40.1% | 66.2% | 45.89% | 74.59% | | [Phantom-1.3B](https://github.com/Phantom-video/Phantom) | 54.89% | 46.67% | 93.3% | 14.29% | 48.56% | 69.43% | 42.48% | 62.5% | | [MAGREF-480P](https://github.com/MAGREF-Video/MAGREF) | 52.51% | 45.02% | 93.17% | 21.81% | 30.83% | 70.47% | 43.04% | 66.9% | | [SkyReels-A2-P14B](https://github.com/SkyworkAI/SkyReels-A2) | 52.25% | 39.41% | 87.93% | 25.6% | 45.95% | 64.54% | 43.75% | 60.32% | | [Vidu2.0(20250503)](https://www.vidu.cn/) | 51.95% | 41.48% | 90.45% | 13.52% | 35.11% | 67.57% | 43.37% | 65.88% | | [Pika2.1(20250503)](https://pika.art/) | 51.88% | 46.88% | 87.06% | 24.71% | 30.38% | 69.19% | 45.4% | 63.32% | | [VACE-1.3B](https://github.com/ali-vilab/VACE) | 49.89% | 48.24% | 97.2% | 18.83% | 20.57% | 71.26% | 37.91% | 65.46% | | [VACE-P1.3B](https://github.com/ali-vilab/VACE) | 48.98% | 47.34% | 96.8% | 12.03% | 16.59% | 71.38% | 40.19% | 64.31% | ### BibTeX ```bibtex @article{li2025bindweave, title={BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration}, author={Li, Zhaoyang and Qian, Dongjun and Su, Kai and Diao, Qishuai and Xia, Xiangyang and Liu, Chang and Yang, Wenfei and Zhang, Tianzhu and Yuan, Zehuan}, journal={arXiv preprint arXiv:2510.00438}, year={2025} }