Collaborative Face Experts Fusion in Video Generation: Boosting Identity Consistency Across Large Face Poses

Abstract

Current video generation models struggle with identity preservation under large face poses, primarily facing two challenges: the difficulty in exploring an effective mechanism to integrate identity features into DiT architectures, and the lack of targeted coverage of large face poses in existing open-source video datasets. To address these, we present two key innovations. First, we propose Collaborative Face Experts Fusion (CoFE), which dynamically fuses complementary signals from three specialized experts within the DiT backbone: an identity expert that captures cross-pose invariant features, a semantic expert that encodes high-level visual context, and a detail expert that preserves pixel-level attributes such as skin texture and color gradients. Second, we introduce a data curation pipeline comprising three key components: Face Constraints to ensure diverse large-pose coverage, Identity Consistency to maintain stable identity across frames, and Speech Disambiguation to align textual captions with actual speaking behavior. This pipeline yields LaFID-180K, a large-scale dataset of pose-annotated video clips designed for identity-preserving video generation. Experimental results on several benchmarks demonstrate that our approach significantly outperforms state-of-the-art methods in face similarity, FID, and CLIP semantic alignment.

Method

Data Process

Experiments

Visualization Comparison on LaFID-Bench and VIP-Test

Visualization Comparison with Closed-Source Models

Citation

@article{wang2025large,
  title={From Large Angles to Consistent Faces: Identity-Preserving Video Generation via Mixture of Facial Experts},
  author={Wang, Yuji and Li, Moran and Hu, Xiaobin and Yi, Ran and Zhang, Jiangning and Xu, Chengming and Cao, Weijian and Wang, Yabiao and Wang, Chengjie and Ma, Lizhuang},
  journal={arXiv e-prints},
  pages={arXiv--2508},
  year={2025}
}