Generative Adversarial Networks for photo to Hayao Miyazaki style cartoons

Andersson, Filip; Arvidsson, Simon

doi:10.48550/arxiv.2005.07702

Cited by 2 publications

(1 citation statement)

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“…We compared the proposed method with various models, including U-GAT-IT [ 1 ], CycleGAN [ 11 ], UNIT [ 22 ], CartoonGAN [ 52 ], Hneg-SRC [ 53 ], and the pre-trained weight of Stable Diffusion v1.5 [ 54 ] obtained from runwayml. It is worth noting that our analysis was not limited to image-to-image translation models exclusively.…”

Section: Methodsmentioning

confidence: 99%

Progressively Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

Lee

et al. 2023

Sensors

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Unsupervised image-to-image translation has received considerable attention due to the recent remarkable advancements in generative adversarial networks (GANs). In image-to-image translation, state-of-the-art methods use unpaired image data to learn mappings between the source and target domains. However, despite their promising results, existing approaches often fail in challenging conditions, particularly when images have various target instances and a translation task involves significant transitions in shape and visual artifacts when translating low-level information rather than high-level semantics. To tackle the problem, we propose a novel framework called Progressive Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization (PRO-U-GAT-IT) for the unsupervised image-to-image translation task. In contrast to existing attention-based models that fail to handle geometric transitions between the source and target domains, our model can translate images requiring extensive and holistic changes in shape. Experimental results show the superiority of the proposed approach compared to the existing state-of-the-art models on different datasets.

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