I2S2: Image-to-Scene Sketch Translation Using Conditional Input and Adversarial Networks

McGonigle, Daniel; Wang, Tianyang; Yuan, Juefei; He, Kai; Li, Bo

doi:10.1109/ictai50040.2020.00123

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Hence, a low FID value denotes well-generated images. The FID is prevalently used in GAN-based image generation research [25,34,35], comparing the distribution of generated images to GT images, to assess their similarity.…”

Section: Metricmentioning

confidence: 99%

Portrait Sketch Generative Model for Misaligned Photo-to-Sketch Dataset

Kim,

Yang

2023

Mathematics

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A deep-learning-based model for generating line-based portrait sketches from portrait photos is proposed in this paper. The misalignment problem is addressed by the introduction of a novel loss term, designed to tolerate misalignments between Ground Truth sketches and generated sketches. Artists’ sketching strategies are mimicked by dividing the portrait into face and hair regions, with separate models trained for each region, and the outcomes subsequently combined. Our contributions include the resolution of misalignment between photos and artist-created sketches, and high-quality sketch results via region-based model training. The experimental results show the effectiveness of our approach in generating convincing portrait sketches, with both quantitative and visual comparisons to State-of-the-Art techniques. The quantitative comparisons demonstrate that our method preserves the identity of the input portrait photos, while applying the style of Ground Truth sketch.

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Section: Metricmentioning

confidence: 99%

Portrait Sketch Generative Model for Misaligned Photo-to-Sketch Dataset

Kim,

Yang

2023

Mathematics

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Unsupervised self-attention lightweight photo-to-sketch synthesis with feature maps

Zhong

Chen

Liu

et al. 2023

Journal of Visual Communication and Image Representation

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A Transfer Learning for Line-Based Portrait Sketch

Kim

Yang

2022

Mathematics

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This paper presents a transfer learning-based framework that produces line-based portrait sketch images from portraits. The proposed framework produces sketch images using a GAN architecture, which is trained through a pseudo-sketch image dataset. The pseudo-sketch image dataset is constructed from a single artist-created portrait sketch using a style transfer model with a series of postprocessing schemes. The proposed framework successfully produces portrait sketch images for portraits of various poses, expressions and illuminations. The excellence of the proposed model is proved by comparing the produced results with those from the existing works.

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I2S2: Image-to-Scene Sketch Translation Using Conditional Input and Adversarial Networks

Cited by 3 publications

References 15 publications

Portrait Sketch Generative Model for Misaligned Photo-to-Sketch Dataset

Portrait Sketch Generative Model for Misaligned Photo-to-Sketch Dataset

Unsupervised self-attention lightweight photo-to-sketch synthesis with feature maps

A Transfer Learning for Line-Based Portrait Sketch

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