title: Magic Mirror: ID-Preserved Video Generation in Video Diffusion
    Transformers
summary: 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/
paper_authors._id: 677df1e52f09432f00e506ae
paper_authors.name: Yuechen Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-08T09:44:33.746Z
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paper_authors.fullname: zhang yuechen
paper_authors.type: user
paper_authors.user: julianjuaner
paper_authors._id: 677df1e52f09432f00e506af
paper_authors.name: Yaoyang Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: YaoYang Liu
paper_authors.type: user
paper_authors.user: LazySheeep
paper_authors._id: 677df1e52f09432f00e506b0
paper_authors.name: Bin Xia
paper_authors._id: 677df1e52f09432f00e506b1
paper_authors.name: Bohao Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-08T13:56:25.937Z
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paper_authors.fullname: Bohao Peng
paper_authors.type: user
paper_authors.user: BoHao0326
paper_authors._id: 677df1e52f09432f00e506b2
paper_authors.name: Zexin Yan
paper_authors._id: 677df1e52f09432f00e506b3
paper_authors.name: Eric Lo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-08T13:56:05.809Z
paper_authors._id: 6447485270338c0376087917
paper_authors.avatarUrl: /avatars/359d17963a6d1c5f1fddf027169332a5.svg
paper_authors.fullname: Eric Lo
paper_authors.type: user
paper_authors.user: ericlo
paper_authors._id: 677df1e52f09432f00e506b4
paper_authors.name: Jiaya Jia
paper_title: Magic Mirror: ID-Preserved Video Generation in Video Diffusion
    Transformers
paper_summary: 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/
paper_ai_keywords: video diffusion models text-to-video generation dual-branch facial feature extractor lightweight cross-modal adapter Conditioned Adaptive Normalization two-stage training strategy synthetic identity pairs