You're currently viewing an old version of this dataset. To see the current version, click here.

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.

Data and Resources

This dataset has no data

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

Private DOI This DOI is not yet resolvable.
It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

Field	Value
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