ToonAging: Face Re-Aging upon Artistic Portrait Style Transfer

• URL: http://arxiv.org/abs/2402.02733v4
• Date: Tue, 28 May 2024 05:00:41 GMT
• Title: ToonAging: Face Re-Aging upon Artistic Portrait Style Transfer
• Authors: Bumsoo Kim, Abdul Muqeet, Kyuchul Lee, Sanghyun Seo,
• Abstract summary: We introduce a novel one-stage method for face re-aging combined with portrait style transfer. We leverage existing face re-aging and style transfer networks, both trained within the same PR domain. Our method offers greater flexibility compared to domain-level fine-tuning approaches.
• Score: 6.305926064192544
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 vectors, each responsible for managing aging-related attributes and NPR appearance. By adopting an exemplar-based approach, our method offers greater flexibility compared to domain-level fine-tuning approaches, which typically require separate training or fine-tuning for each domain. This effectively addresses the limitation of requiring paired datasets for re-aging and domain-level, data-driven approaches for stylization. Our experiments show that our model can effortlessly generate re-aged images while simultaneously transferring the style of examples, maintaining both natural appearance and controllability.

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