title: MagicID: Hybrid Preference Optimization for ID-Consistent and
    Dynamic-Preserved Video Customization
summary: Video identity customization seeks to produce high-fidelity videos that
  maintain consistent identity and exhibit significant dynamics based on users'
  reference images. However, existing approaches face two key challenges:
  identity degradation over extended video length and reduced dynamics during
  training, primarily due to their reliance on traditional self-reconstruction
  training with static images. To address these issues, we introduce
  MagicID, a novel framework designed to directly promote the
  generation of identity-consistent and dynamically rich videos tailored to user
  preferences. Specifically, we propose constructing pairwise preference video
  data with explicit identity and dynamic rewards for preference learning,
  instead of sticking to the traditional self-reconstruction. To address the
  constraints of customized preference data, we introduce a hybrid sampling
  strategy. This approach first prioritizes identity preservation by leveraging
  static videos derived from reference images, then enhances dynamic motion
  quality in the generated videos using a Frontier-based sampling method. By
  utilizing these hybrid preference pairs, we optimize the model to align with
  the reward differences between pairs of customized preferences. Extensive
  experiments show that MagicID successfully achieves consistent identity and
  natural dynamics, surpassing existing methods across various metrics.
paper_authors._id: 67dcc2e10df3501c657ef478
paper_authors.name: Hengjia Li
paper_authors._id: 67dcc2e10df3501c657ef479
paper_authors.name: Lifan Jiang
paper_authors._id: 67dcc2e10df3501c657ef47a
paper_authors.name: Xi Xiao
paper_authors._id: 67dcc2e10df3501c657ef47b
paper_authors.name: Tianyang Wang
paper_authors._id: 67dcc2e10df3501c657ef47c
paper_authors.name: Hongwei Yi
paper_authors._id: 67dcc2e10df3501c657ef47d
paper_authors.name: Boxi Wu
paper_authors._id: 67dcc2e10df3501c657ef47e
paper_authors.name: Deng Cai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T15:01:22.490Z
paper_authors._id: 646735a1946476c5d214e5b2
paper_authors.avatarUrl: /avatars/23392fda23fa6288fcb309f7eaa73472.svg
paper_authors.fullname: Deng Cai
paper_authors.type: user
paper_authors.user: jcyk
paper_title: MagicID: Hybrid Preference Optimization for ID-Consistent and
    Dynamic-Preserved Video Customization
paper_summary: Video identity customization seeks to produce high-fidelity videos that
  maintain consistent identity and exhibit significant dynamics based on users'
  reference images. However, existing approaches face two key challenges:
  identity degradation over extended video length and reduced dynamics during
  training, primarily due to their reliance on traditional self-reconstruction
  training with static images. To address these issues, we introduce
  MagicID, a novel framework designed to directly promote the
  generation of identity-consistent and dynamically rich videos tailored to user
  preferences. Specifically, we propose constructing pairwise preference video
  data with explicit identity and dynamic rewards for preference learning,
  instead of sticking to the traditional self-reconstruction. To address the
  constraints of customized preference data, we introduce a hybrid sampling
  strategy. This approach first prioritizes identity preservation by leveraging
  static videos derived from reference images, then enhances dynamic motion
  quality in the generated videos using a Frontier-based sampling method. By
  utilizing these hybrid preference pairs, we optimize the model to align with
  the reward differences between pairs of customized preferences. Extensive
  experiments show that MagicID successfully achieves consistent identity and
  natural dynamics, surpassing existing methods across various metrics.
paper_ai_summary: MagicID addresses video identity customization by generating consistent yet dynamic videos using preference learning with hybrid sampling strategies.
paper_ai_keywords: video identity customization identity degradation dynamics self-reconstruction pairwise preference video data identity rewards dynamic rewards preference learning hybrid sampling strategy Frontier-based sampling method reward differences video generation