Phantom: Subject-Consistent Video Generation via Cross-Modal Alignment
Lijie Liu ^* , Tianxiang Ma ^* , Bingchuan Li ^{* †}, Zhuowei Chen ^* , Jiawei Liu, Qian He, Xinglong Wu
^* Equal contribution, ^† Project lead
Intelligent Creation Team, ByteDance

Phantom is a unified video generation framework for single and multi-subject references, built on existing text-to-video and image-to-video architectures. It achieves cross-modal alignment using text-image-video triplet data by redesigning the joint text-image injection model. Additionally, it emphasizes subject consistency in human generation while enhancing ID-preserving video generation.

• Identity Preserving Video Generation.
• Single Reference Subject-to-Video Generation.
• Multi-Reference Subject-to-Video Generation.

@article{liu2025phantom,
  title={Phantom: Subject-Consistent Video Generation via Cross-Modal Alignment},
  author={Liu, Lijie and Ma, Tianxaing and Li, Bingchuan and Chen, Zhuowei and Liu, Jiawei and He, Qian and Wu, Xinglong},
  journal={arXiv preprint arXiv:2502.11079},
  year={2025}
}