Magic Mirror: ID-Preserved Video Generation in Video Diffusion
  Transformers
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/
677df1e52f09432f00e506ae
Yuechen Zhang
claimed_verified
2025-01-08T09:44:33.746Z
6418554a0956be7233a1023e
https://cdn-avatars.huggingface.co/v1/production/uploads/6418554a0956be7233a1023e/9EKN0GoOpcDbvBDmAQEJf.png
zhang yuechen
user
julianjuaner
677df1e52f09432f00e506af
Yaoyang Liu
admin_assigned
2025-01-08T13:55:13.065Z
662d968eac05b4f7c280d6f9
/avatars/59d55142f33f650d9116a04110db839e.svg
YaoYang Liu
user
LazySheeep
677df1e52f09432f00e506b0
Bin Xia
677df1e52f09432f00e506b1
Bohao Peng
admin_assigned
2025-01-08T13:56:25.937Z
673a10f911b7efeeedabc252
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/T7ySn7F0pTVCvRdcvMz3d.png
Bohao Peng
user
BoHao0326
677df1e52f09432f00e506b2
Zexin Yan
677df1e52f09432f00e506b3
Eric Lo
admin_assigned
2025-01-08T13:56:05.809Z
6447485270338c0376087917
/avatars/359d17963a6d1c5f1fddf027169332a5.svg
Eric Lo
user
ericlo
677df1e52f09432f00e506b4
Jiaya Jia
Magic Mirror: ID-Preserved Video Generation in Video Diffusion
  Transformers
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/
Magic Mirror framework uses Video Diffusion Transformers to generate identity-preserved videos with natural motion and high quality, overcoming limitations of existing methods.
Video Diffusion Transformers dual-branch facial feature extractor identity and structural features lightweight cross-modal adapter Conditioned Adaptive Normalization two-stage training strategy synthetic identity pairs