MagicID: Hybrid Preference Optimization for ID-Consistent and
  Dynamic-Preserved Video Customization
Authors: name: Hengjia Li, name: Lifan Jiang, name: Xi Xiao, name: Tianyang Wang, name: Hongwei Yi, name: Boxi Wu, name: Deng Cai

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.