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Styledrop: Text-to-image generation in any style
Pre-trained large text-to-image models synthesize impressive images with an appropriate
use of text prompts. However, ambiguities inherent in natural language and out-of …
use of text prompts. However, ambiguities inherent in natural language and out-of …
Styledrop: Text-to-image synthesis of any style
Pre-trained large text-to-image models synthesize impressive images with an appropriate
use of text prompts. However, ambiguities inherent in natural language, and out-of …
use of text prompts. However, ambiguities inherent in natural language, and out-of …
Z*: Zero-shot style transfer via attention reweighting
Despite the remarkable progress in image style transfer formulating style in the context of art
is inherently subjective and challenging. In contrast to existing methods this study shows that …
is inherently subjective and challenging. In contrast to existing methods this study shows that …
Stylizedgs: Controllable stylization for 3d gaussian splatting
As XR technology continues to advance rapidly, 3D generation and editing are increasingly
crucial. Among these, stylization plays a key role in enhancing the appearance of 3D …
crucial. Among these, stylization plays a key role in enhancing the appearance of 3D …
Instastyle: Inversion noise of a stylized image is secretly a style adviser
Stylized text-to-image generation focuses on creating images from textual descriptions while
adhering to a style specified by reference images. However, subtle style variations within …
adhering to a style specified by reference images. However, subtle style variations within …
Learning to evaluate the artness of AI-generated images
Assessing the artness of AI-generated images continues to be a challenge within the realm
of image generation. Most existing metrics cannot be used to perform instance-level and …
of image generation. Most existing metrics cannot be used to perform instance-level and …
Evaluation in neural style transfer: A review
The field of neural style transfer (NST) has witnessed remarkable progress in the past few
years, with approaches being able to synthesize artistic and photorealistic images and …
years, with approaches being able to synthesize artistic and photorealistic images and …
Bridging the metrics gap in image style transfer: A comprehensive survey of models and criteria
X Zhou, Y Zheng, J Yang - Neurocomputing, 2025 - Elsevier
Image style transfer is a technique that combines the content of a real photograph with the
artistic style of another image to create a new and stylized image. In this paper, we aim to …
artistic style of another image to create a new and stylized image. In this paper, we aim to …
: Zero-shot Style Transfer via Attention Rearrangement
Despite the remarkable progress in image style transfer, formulating style in the context of art
is inherently subjective and challenging. In contrast to existing learning/tuning methods, this …
is inherently subjective and challenging. In contrast to existing learning/tuning methods, this …
Dual-Encoding Matching Adversarial Learning for Image Cartoonlization
Generative Adversarial Network (GAN)-based image cartoonization has made great
progress. They usually use a “single-encoding adversarial feedback architecture” to …
progress. They usually use a “single-encoding adversarial feedback architecture” to …