Interactive Style Transfer: Towards Styling User-Specified Object

Virtusio, John Jethro; Talavera, Arces; Tan, Daniel Stanley; Hua, Kai-Lung; Azcarraga, Arnulfo P.

doi:10.1109/vcip.2018.8698689

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…e CNN model needs a huge amount of data sets that are very difficult in medical based image processing. For this problem, the image generation is used to increase the computer vision [24]. A new engine is developed to exploit the network to confine the synthetic information.…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Nets Learning Classification for Artistic Style Transfer

Kumar

Julie

Robinson

et al. 2022

Scientific Programming

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Humans have mastered the skill of creativity for many decades. The process of replicating this mechanism is introduced recently by using neural networks which replicate the functioning of human brain, where each unit in the neural network represents a neuron, which transmits the messages from one neuron to other, to perform subconscious tasks. Usually, there are methods to render an input image in the style of famous art works. This issue of generating art is normally called nonphotorealistic rendering. Previous approaches rely on directly manipulating the pixel representation of the image. While using deep neural networks which are constructed using image recognition, this paper carries out implementations in feature space representing the higher levels of the content image. Previously, deep neural networks are used for object recognition and style recognition to categorize the artworks consistent with the creation time. This paper uses Visual Geometry Group (VGG16) neural network to replicate this dormant task performed by humans. Here, the images are input where one is the content image which contains the features you want to retain in the output image and the style reference image which contains patterns or images of famous paintings and the input image which needs to be style and blend them together to produce a new image where the input image is transformed to look like the content image but “sketched” to look like the style image.

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Section: Related Workmentioning

confidence: 99%

Deep Convolutional Nets Learning Classification for Artistic Style Transfer

Kumar

Julie

Robinson

et al. 2022

Scientific Programming

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“…• Bo Ren is the corresponding author (rb@nankai.edu.cn). [45]. Although these NST methods, even with global adjustment, can create artworks, they can not provide fine-grained controls in artistic editing, and are not in line with natural drawing logic.…”

Section: Introductionmentioning

confidence: 99%

Interactive Style Transfer: All is Your Palette

Zheng¹,

Zhang²,

Zhang³

et al. 2022

Preprint

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Neural style transfer (NST) can create impressive artworks by transferring reference style to content image. Current imageto-image NST methods are short of fine-grained controls, which are often demanded by artistic editing. To mitigate this limitation, we propose a drawing-like interactive style transfer (IST) method, by which users can interactively create a harmonious-style image. Our IST method can serve as a brush, dip style from anywhere, and then paint to any region of the target content image. To determine the action scope, we formulate a fluid simulation algorithm, which takes styles as pigments around the position of brush interaction, and diffusion in style or content images according to the similarity maps. Our IST method expands the creative dimension of NST. By dipping and painting, even employing one style image can produce thousands of eye-catching works. The demo video is available in supplementary files or in http://mmcheng.net/ist.

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“…Procedural content generation (PCG) is the systematic automation of producing content merging user-generated sprites, audio, and visuals using algorithmic approaches in order to create enhanced, diverse and automatic content in a fast and transitive way [4]. Neural style transfer is one of the PCG approaches that create diverse artistic styles [5][6][7][8][9][10][11] and get remarkable results, mainly in the image processing domain. Neural style transfer has become a highly researched topic in recent years due to deep learning practices [5,11].…”

Section: Introductionmentioning

confidence: 99%

“…One solution to this problem is to associate semantic labeling of the input and style images with maximizing the subregion mapping, as proposed in [6]. In [7], a different approach in which the foreground segmentation was combined with the neural style transfer was used. The main idea was to style only the user-specified object, which was done by first styling the whole image and then separately segmenting out the object to overlap the segmented and styled image further.…”

Section: Introductionmentioning

confidence: 99%

İki Boyutlu Video Oyunlarında Sinir Stili Aktarımı Kullanarak Otomatik Oyun Mekaniği ve Estetiği Üretimi

Şen

Küçükkayki

Sürer

2021

Bilişim Teknolojileri Dergisi

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Video oyunu araştırması, karmaşık yöntemlerin ve algoritmaların geliştirildiği, sürekli değişmekte olan, dinamik bir alandır. Prosedürel içerik üretimi, kullanıcı tarafından oluşturulan parçaları video oyunu içeriğini otomatikleştirmek ve geliştirmek için algoritmalarla birleştirmeyi amaçlamakta ve bu yöntemlerin temelini oluşturmaktadır. Bununla birlikte, sonuçlar oyun mekaniğine ve oyunun oynanış biçimine değil, çoğunlukla oyun estetiğine yansımaktadır. Bu çalışmada, "tuval olarak oyun sahnesi" konsepti ile kullanıma hazır çarpıştırıcılar ve oyun estetiğini geliştiren, sanatsal açıdan farklı stiller kullanarak iki boyutlu oyun seviyesindeki bir görüntüyü basit bir prototip oyun geliştirme ortamına dönüştürebilen yöntem ve süreç sunulmaktadır. Bu amaçla, giriş oyun seviyesi görüntüsünün kenar ve renk bazlı özellikleri Canny kenar belirleme, basit doğrusal yinelemeli kümeleme ve Felzenszwalb segmentasyonu kullanılarak çıkarılmaktadır. Daha sonra, Unity oyun motoru, mekansal kontrol ile oyun seviyesinin stilinin aktarıldığı kenar ve renk özelliklerine göre çarpıştırıcılar oluşturmak için kullanılmaktadır. Farklı sinir stil transfer algoritmalarının sonuçları, Super Mario, Lode Runner ve Kid Icarus gibi oyunlar üzerinde karşılaştırılmakta ve tartışılmaktadır. Sonuçlar, bu çalışmanın oyun mekaniği ve oyun estetiğine odaklanarak iki boyutlu video oyunu geliştirmeyi kolaylaştırma potansiyeline sahip bir araç olduğunu göstermektedir.

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Interactive Style Transfer: Towards Styling User-Specified Object

Cited by 11 publications

References 4 publications

Deep Convolutional Nets Learning Classification for Artistic Style Transfer

Deep Convolutional Nets Learning Classification for Artistic Style Transfer

Interactive Style Transfer: All is Your Palette

İki Boyutlu Video Oyunlarında Sinir Stili Aktarımı Kullanarak Otomatik Oyun Mekaniği ve Estetiği Üretimi

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