Pluralistic Face Aging with CLIP-driven Pluralistic Aging Diffusion Autoencoder

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.

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Cite this as

Peipei Li, Rui Wang, Huaibo Huang, Ran He, Zhaofeng He (2024). Dataset: Pluralistic Face Aging with CLIP-driven Pluralistic Aging Diffusion Autoencoder. https://doi.org/10.57702/e7578pe7

DOI retrieved: December 2, 2024

Additional Info

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Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2303.11086
Author Peipei Li
More Authors
Rui Wang
Huaibo Huang
Ran He
Zhaofeng He
Homepage https://github.com/siriusdemon/pytorch-DEX