Pluralistic Aging Diffusion Autoencoder

• URL: http://arxiv.org/abs/2303.11086v2
• Date: Thu, 24 Aug 2023 03:53:35 GMT
• Title: Pluralistic Aging Diffusion Autoencoder
• Authors: Peipei Li, Rui Wang, Huaibo Huang, Ran He, Zhaofeng He
• Abstract summary: Face aging is an ill-posed problem because multiple plausible aging patterns may correspond to a given input. This paper proposes a novel CLIP-driven Pluralistic Aging Diffusion Autoencoder to enhance the diversity of aging patterns.
• Score: 63.50599304294062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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