MuseFace：基于扩散的掩码生成方法的文本驱动人脸编辑
MuseFace: Text-driven Face Editing via Diffusion-based Mask Generation Approach
AI总结：
提出MuseFace框架，通过集成Text-to-Mask扩散模型与语义感知编辑模型，仅依赖文本提示即可生成细粒度语义掩码并实现精确的人脸编辑，兼顾多样性、可控性与灵活性，实验证明其具备优越的高保真性能。
摘要：
人脸编辑修改人脸的外观，在个性化和增强个人图像中发挥关键作用。尽管许多工作在文本驱动的人脸编辑中取得了显著成功，但它们仍面临重大挑战，因为没有任何方法能同时满足多样性、可控性和灵活性。为应对这一挑战，我们提出了MuseFace，一个文本驱动的人脸编辑框架，仅依赖文本提示即可实现人脸编辑。具体而言，MuseFace集成了Text-to-Mask扩散模型和语义感知人脸编辑模型，能够直接从文本生成细粒度语义掩码并执行人脸编辑。Text-to-Mask扩散模型为框架提供了多样性和灵活性，而语义感知人脸编辑模型则确保了框架的可控性。我们的框架能创建细粒度语义掩码，使精确的人脸编辑成为可能，并显著增强了人脸编辑模型的可控性和灵活性。大量实验表明，MuseFace实现了优越的高保真性能。
英文摘要：
Face editing modifies the appearance of face, which plays a key role in customization and enhancement of personal images. Although much work have achieved remarkable success in text-driven face editing, they still face significant challenges as none of them simultaneously fulfill the characteristics of diversity, controllability and flexibility. To address this challenge, we propose MuseFace, a text-driven face editing framework, which relies solely on text prompt to enable face editing. Specifically, MuseFace integrates a Text-to-Mask diffusion model and a semantic-aware face editing model, capable of directly generating fine-grained semantic masks from text and performing face editing. The Text-to-Mask diffusion model provides \textit{diversity} and \textit{flexibility} to the framework, while the semantic-aware face editing model ensures \textit{controllability} of the framework. Our framework can create fine-grained semantic masks, making precise face editing possible, and significantly enhancing the controllability and flexibility of face editing models. Extensive experiments demonstrate that MuseFace achieves superior high-fidelity performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
