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.

翻译：人脸老化是一个不适定问题，因为给定输入可能对应多种合理的老化模式。现有方法通常仅产生确定性估计。本文提出一种新颖的CLIP驱动的多元老化扩散自编码器（PADA），以增强老化模式的多样性。首先，我们利用扩散模型通过序列化去噪逆过程生成多样化的低层级老化细节。其次，我们提出概率老化嵌入（PAE）来捕捉多样化的高层级老化模式，该方法将年龄信息表示为公共CLIP潜在空间中的概率分布，并设计了文本引导的KL散度损失函数来指导该学习过程。我们的方法能够基于开放世界老化文本和任意未见人脸图像实现多元人脸老化。定性与定量实验表明，该方法能生成更多样化且高质量的老化结果。