MagicMirror: ID-Preserved Video Generation in Video Diffusion Transformers

Understand

We present MagicMirror, 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.