https://github.com/OmarSZamzam/PixelShuffler
공식 코드PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Style Transfer is a technique in computer vision and graphics that involves generating a new image by combining the content of one image with the style of another image. The goal of style transfer is to create an image that preserves the content of the original image while applying the visual style of another image.
<span style="color:grey; opacity: 0.6">( Image credit: A Neural Algorithm of Artistic Style )</span>
The "T" horizontal strip can mimic the back of a sofa with a delicate cushion or details of the uphols or appliances with the color button.
The "T" vertical strip can show a feet or arm of the sofa, shiny, yet firm.
Put "P" next to "T", your curve to delicately with the top "T." It is intertwined. The circular part of "P" can show a cushion or a curved chair and synchronize the subject of furniture.
Make sure "P" is visually relying on "T", which reflects the relationship of cohesion and balance.
"B" can be aligned as a pair of cushions or a modern chair, with mild curves with glossy and modern aesthetics.
"I" can be a symbol of a shiny furniture or a vertical light bar and completes the shapes without overburdess them.
Color palette 4:
Includes soft soil colors such as beige, top and gray shades, along with silent or silver gold tips to touch elegance.
Consider a slope effect to enhance modernity, to keep colors elegant and complex.
Use the overlap or intertwined edges that the letters meet for the symbol of unity.
The plan should allow viewers to distinguish each letter while feeling part of the same "structure".
Use delicate geometric patterns or textures that mimic fabrics or furniture materials such as wood seeds or woven fibers.
These patterns must remain minimalist and focus on highlighting the logo, while maintaining communication.
While it deals with the subject of furniture and design, this concept conveys modernity, creativity and professional. If you like, I can create a draft design for better visualization.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
Interactive Video Stylization Using Few-Shot Patch-Based Training
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
Parameter-Free Style Projection for Arbitrary Style Transfer
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer
UniVST: A Unified Framework for Training-free Localized Video Style Transfer
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Controllable Artistic Text Style Transfer via Shape-Matching GAN
MOVE: Effective and Harmless Ownership Verification via Embedded External Features
Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity
Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer
Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation
Decoupling Global and Local Representations via Invertible Generative Flows
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE
SinIR: Efficient General Image Manipulation with Single Image Reconstruction
Structured Content Preservation for Unsupervised Text Style Transfer
Weakly-supervised Caricature Face Parsing through Domain Adaptation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.