Face aging is an ill-posed problem because multiple plausible aging patterns may correspond to a given input. Most existing methods often produce one deterministic estimation. This paper proposes a novel CLIP-driven Pluralistic Aging Diffusion Autoencoder (PADA) to enhance the diversity of aging patterns. First, we employ diffusion models to generate diverse low-level aging details via a sequential denoising reverse process. Second, we present Probabilistic Aging Embedding (PAE) to capture diverse high-level aging patterns, which represents age information as probabilistic distributions in the common CLIP latent space. A text-guided KL-divergence loss is designed to guide this learning. Our method can achieve pluralistic face aging conditioned on open-world aging texts and arbitrary unseen face images. Qualitative and quantitative experiments demonstrate that our method can generate more diverse and high-quality plausible aging results.

本文提出了一种CLIP-driven Pluralistic Aging Diffusion Autoencoder（PADA）方法，以增强面部衰老模式的多样性，其中使用扩散模型生成不同的低级衰老细节，并提出概率衰老嵌入（PAE）以捕获不同的高级衰老模式，在通用的CLIP潜在空间中使用文本引导KL散度损失进行学习，该方法可以根据任意未见面部图像和开放世界衰老文本实现多元化的面部衰老。

多元化老化扩散自编码器