Abstract
Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable. Additionally, many methods require supplementary data such as facial landmarks and masks to guide the synthesis process. In contrast, our approach uses diffusion models with only a reconstruction loss, eliminating the need for facial landmarks or masks while still producing images with intricate, fine-grained details. We validated our results on two public benchmarks through both quantitative and qualitative evaluations. Our model achieves state-of-the-art performance in three key areas: identity anonymization, facial attribute preservation, and image quality. Beyond its primary function of anonymization, our model can also perform face swapping tasks by incorporating an additional facial image as input, demonstrating its versatility and potential for diverse applications. Our code and models are available at this https URL .
Abstract (translated)
当前的面部匿名化技术通常依赖于由人脸识别模型计算出的身份损失，这可能会导致不准确和不可靠的结果。此外，许多方法需要额外的数据，如面部特征点和遮罩来指导合成过程。相比之下，我们的方法仅使用扩散模型和重建损失，不需要面部特征点或遮罩，同时仍能生成具有复杂精细细节的图像。我们在两个公开基准上通过定量和定性评估验证了结果。我们的模型在三个关键领域达到了最先进的性能：身份匿名化、面部属性保留和图像质量。除了主要的匿名功能外，通过将额外的面部图像作为输入，我们的模型还可以执行面部交换任务，展示了其多样性和潜在的应用前景。我们的代码和模型可以在以下链接中获取: [此 HTTPS URL]。
URL
https://arxiv.org/abs/2411.00762
