High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning

Face swapping aims to seamlessly transfer a source facial identity onto a target while preserving target attributes such as pose and expression. Diffusion models, known for their superior generative capabilities, have recently shown promise in advancing face-swapping quality. This paper addresses two key challenges in diffusion-based face swapping: the prioritized preservation of identity over target attributes and the inherent conflict between identity and attribute conditioning. To tackle these issues, we introduce an identity-constrained attribute-tuning framework for face swapping that first ensures identity preservation and then fine-tunes for attribute alignment, achieved through a decoupled condition injection. We further enhance fidelity by incorporating identity and adversarial losses in a post-training refinement stage. Our proposed identity-constrained diffusion-based face-swapping model outperforms existing methods in both qualitative and quantitative evaluations, demonstrating superior identity similarity and attribute consistency, achieving a new state-of-the-art performance in high-fidelity face swapping.

@article{he2025_2503.22179,
  title={ High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning },
  author={ Dailan He and Xiahong Wang and Shulun Wang and Guanglu Song and Bingqi Ma and Hao Shao and Yu Liu and Hongsheng Li },
  journal={arXiv preprint arXiv:2503.22179},
  year={ 2025 }
}