MagicID: Hybrid Preference Optimization for ID-Consistent and
  Dynamic-Preserved Video Customization
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.
67dcc2e10df3501c657ef478
Hengjia Li
67dcc2e10df3501c657ef479
Lifan Jiang
67dcc2e10df3501c657ef47a
Xi Xiao
67dcc2e10df3501c657ef47b
Tianyang Wang
67dcc2e10df3501c657ef47c
Hongwei Yi
67dcc2e10df3501c657ef47d
Boxi Wu
67dcc2e10df3501c657ef47e
Deng Cai
admin_assigned
2025-03-21T15:01:22.490Z
646735a1946476c5d214e5b2
/avatars/23392fda23fa6288fcb309f7eaa73472.svg
Deng Cai
user
jcyk
MagicID: Hybrid Preference Optimization for ID-Consistent and
  Dynamic-Preserved Video Customization
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.
MagicID addresses video identity customization by generating consistent yet dynamic videos using preference learning with hybrid sampling strategies.
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