ToonAging: Face Re-Aging upon Artistic Portrait Style Transfer
Bumsoo Kim, Abdul Muqeet, Kyuchul Lee, Sanghyun Seo

TL;DR
This paper presents a novel one-stage face re-aging method that combines portrait style transfer for non-photorealistic images, enabling seamless editing of apparent age while preserving style and facial attributes.
Contribution
Introduces a unified generative approach for face re-aging and style transfer in non-photorealistic images, eliminating the need for domain-specific fine-tuning or paired datasets.
Findings
Effective in generating re-aged images with style transfer in a single step
Maintains facial attributes and natural appearance in stylized re-aging
Flexible exemplar-based approach surpasses domain-level fine-tuning limitations
Abstract
Face re-aging is a prominent field in computer vision and graphics, with significant applications in photorealistic domains such as movies, advertising, and live streaming. Recently, the need to apply face re-aging to non-photorealistic images, like comics, illustrations, and animations, has emerged as an extension in various entertainment sectors. However, the lack of a network that can seamlessly edit the apparent age in NPR images has limited these tasks to a naive, sequential approach. This often results in unpleasant artifacts and a loss of facial attributes due to domain discrepancies. In this paper, we introduce a novel one-stage method for face re-aging combined with portrait style transfer, executed in a single generative step. We leverage existing face re-aging and style transfer networks, both trained within the same PR domain. Our method uniquely fuses distinct latent…
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Taxonomy
TopicsFace recognition and analysis
