基于扩散编辑的人脸老化
Face Aging via Diffusion-based Editing
AI总结：
本文提出FADING方法，利用大规模语言-图像扩散模型的先验，通过年龄感知微调、潜在噪声反演和注意力控制实现人脸年龄编辑，在老化准确性、属性保持和老化质量上优于现有方法。
摘要：
在本文中，我们解决人脸老化问题：通过将年龄相关的变化融入给定的人脸来生成过去或未来的面部图像。以往的老化方法仅依赖人类面部图像数据集，因此受其固有规模和偏差的限制。这限制了它们可生成的年龄范围，并且无法处理较大的年龄跨度。我们提出FADING，一种通过基于扩散的编辑来解决人脸老化的新方法。我们通过利用大规模语言-图像扩散模型的丰富先验超越了现有方法。首先，我们通过使用年龄感知的微调方案，将预训练的扩散模型专门用于人脸年龄编辑任务。接下来，我们将输入图像反演为潜在噪声，并获得优化的空文本嵌入。最后，我们通过注意力控制执行文本引导的局部年龄编辑。定量和定性分析表明，我们的方法在老化准确性、属性保持和老化质量方面优于现有方法。
英文摘要：
In this paper, we address the problem of face aging: generating past or future facial images by incorporating age-related changes to the given face. Previous aging methods rely solely on human facial image datasets and are thus constrained by their inherent scale and bias. This restricts their application to a limited generatable age range and the inability to handle large age gaps. We propose FADING, a novel approach to address Face Aging via DIffusion-based editiNG. We go beyond existing methods by leveraging the rich prior of large-scale language-image diffusion models. First, we specialize a pre-trained diffusion model for the task of face age editing by using an age-aware fine-tuning scheme. Next, we invert the input image to latent noise and obtain optimized null text embeddings. Finally, we perform text-guided local age editing via attention control. The quantitative and qualitative analyses demonstrate that our method outperforms existing approaches with respect to aging accuracy, attribute preservation, and aging quality.
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。
