用约束身份的扩散模型实现高保真人脸替换，更自然且保留原表情姿态。
High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning
- 分步处理：先保身份，再调姿态表情，避免冲突。
- 在多个数据集上身份相似度提升，属性一致性更好。
- 适合需要高质量人脸替换的影视、社交应用。
人脸替换旨在将源人脸身份无缝迁移到目标上，同时保留目标的姿态和表情等属性。扩散模型凭借其强大的生成能力，在提升人脸替换质量方面展现出潜力。本文针对基于扩散模型的人脸替换中的两大挑战：身份优先于属性的保持，以及身份与属性条件之间的固有冲突，提出一种身份约束的属性微调框架。该框架通过解耦条件注入，先确保身份一致，再精细调整属性对齐。此外，通过后训练阶段引入身份损失和对抗损失，进一步提升生成质量。所提方法在定性和定量评估中均优于现有方法，显著提升了身份相似度与属性一致性，实现了高保真人脸替换的新基准性能。
原文摘要 · Abstract (English)
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.
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