A Common Framework for Interactive Texture Transfer

Men, Yifang; Lian, Zhouhui; Tang, Yingmin; Xiao, Jianguo

doi:10.1109/cvpr.2018.00665

Cited by 26 publications

(37 citation statements)

References 29 publications

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“…[ 14 ] defined a new problem called image analogies to synthesize a new

from B according to the given pair

and A , which allows users to simply provide an exemplar and produce a synthesis result similar to it. Men et al [ 15 ] proposed a common texture transfer framework that regards texture transfer as an image inpainting problem, and produces the target image according to the original image and the semantic map. Specifically, this method first warps the original image according to the semantic map of the original image and target image to obtain the prior target image, then inpaints the warped image using PatchMatch to obtain the final result.…”

Section: State Of the Artmentioning

confidence: 99%

A Foreground-Aware Framework for Local Face Attribute Transfer

Guo

2021

Entropy

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In the context of social media, large amounts of headshot photos are taken everyday. Unfortunately, in addition to laborious editing and modification, creating a visually compelling photographic masterpiece for sharing requires advanced professional skills, which are difficult for ordinary Internet users. Though there are many algorithms automatically and globally transferring the style from one image to another, they fail to respect the semantics of the scene and are unable to allow users to merely transfer the attributes of one or two face organs in the foreground region leaving the background region unchanged. To overcome this problem, we developed a novel framework for semantically meaningful local face attribute transfer, which can flexibly transfer the local attribute of a face organ from the reference image to a semantically equivalent organ in the input image, while preserving the background. Our method involves warping the reference photo to match the shape, pose, location, and expression of the input image. The fusion of the warped reference image and input image is then taken as the initialized image for a neural style transfer algorithm. Our method achieves better performance in terms of inception score (3.81) and Fréchet inception distance (80.31), which is about 10% higher than those of competitors, indicating that our framework is capable of producing high-quality and photorealistic attribute transfer results. Both theoretical findings and experimental results are provided to demonstrate the efficacy of the proposed framework, reveal its superiority over other state-of-the-art alternatives.

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“…[ 14 ] defined a new problem called image analogies to synthesize a new

from B according to the given pair

Section: State Of the Artmentioning

confidence: 99%

A Foreground-Aware Framework for Local Face Attribute Transfer

Guo

2021

Entropy

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“…Gatys et al [9] first propose a parametric NST method using CNN and Gram matrices. Then, researchers propose a series of parametric works to improve the performance on visual quality [10,16,24,33], generating speed [3,13,14,17,19,20,27], and multimedia extension [2,4,12,32].…”

Section: Neural Style Transfermentioning

confidence: 99%

Q-Art Code: Generating Scanning-robust Art-style QR Codes by Deformable Convolution

Niu

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

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“…For example, explored the task of generating special text effects for typography by proposing an optimizationbased model. Based on the traditional texture transfer technique, Men et al [2018] proposed to adopt structure information to effectively guide the synthesis process. More recently, a deep learning based method was reported ] to accelerate the transfer process while maintaining the image quality.…”

Section: Style Transfermentioning

confidence: 99%

Artistic glyph image synthesis via one-stage few-shot learning

et al. 2019

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Fig. 1. (a) An overview of our method, given a few reference samples (5 for English or 30 for Chinese), glyph images of all other characters in the same style can be synthesized. (b) Examples of synthesized English/Chinese glyph images obtained by our proposed AGIS-Net, MC-GAN [Azadi et al. 2018] and TET-GAN [Yang et al. 2019], respectively, please zoom in for better inspection.Automatic generation of artistic glyph images is a challenging task that attracts many research interests. Previous methods either are specifically designed for shape synthesis or focus on texture transfer. In this paper, we propose a novel model, AGIS-Net, to transfer both shape and texture styles in one-stage with only a few stylized samples. To achieve this goal, we first disentangle the representations for content and style by using two encoders, ensuring the multi-content and multi-style generation. Then we utilize two collaboratively working decoders to generate the glyph shape image and its texture image simultaneously. In addition, we introduce a local texture refinement loss to further improve the quality of the synthesized textures. In this manner, our one-stage model is much more efficient and effective than other multi-stage stacked methods. We also propose a large-scale dataset with Chinese glyph images in various shape and texture styles, rendered from 35 professional-designed artistic fonts with 7,326 characters and 2,460 synthetic artistic fonts with 639 characters, to validate the effectiveness and extendability of our method. Extensive experiments on both English and Chinese artistic glyph image datasets demonstrate the superiority of our model in generating high-quality stylized glyph images against other state-of-the-art methods.

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A Common Framework for Interactive Texture Transfer

Cited by 26 publications

References 29 publications

A Foreground-Aware Framework for Local Face Attribute Transfer

A Foreground-Aware Framework for Local Face Attribute Transfer

Q-Art Code: Generating Scanning-robust Art-style QR Codes by Deformable Convolution

Artistic glyph image synthesis via one-stage few-shot learning

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