Magic Mirror: ID-Preserved Video Generation in Video Diffusion
  Transformers
Authors: name: Yuechen Zhang, name: Yaoyang Liu, name: Bin Xia, name: Bohao Peng, name: Zexin Yan, name: Eric Lo, name: Jiaya Jia

We present Magic Mirror, a framework for generating identity-preserved videos
with cinematic-level quality and dynamic motion. While recent advances in video
diffusion models have shown impressive capabilities in text-to-video
generation, maintaining consistent identity while producing natural motion
remains challenging. Previous methods either require person-specific
fine-tuning or struggle to balance identity preservation with motion diversity.
Built upon Video Diffusion Transformers, our method introduces three key
components: (1) a dual-branch facial feature extractor that captures both
identity and structural features, (2) a lightweight cross-modal adapter with
Conditioned Adaptive Normalization for efficient identity integration, and (3)
a two-stage training strategy combining synthetic identity pairs with video
data. Extensive experiments demonstrate that Magic Mirror effectively balances
identity consistency with natural motion, outperforming existing methods across
multiple metrics while requiring minimal parameters added. The code and model
will be made publicly available at:
https://github.com/dvlab-research/MagicMirror/