BindWeave: Subject-Consistent Video Generation via Cross-Modal
  Integration
Authors: name: Zhaoyang Li, name: Dongjun Qian, name: Kai Su, name: Qishuai Diao, name: Xiangyang Xia, name: Chang Liu, name: Wenfei Yang, name: Tianzhu Zhang, name: Zehuan Yuan

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.