Style Transfer
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 5
- 보존된 평가 기록
- 12
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>
- "T" as a sofa:
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.
- Merge "P":
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.
- Coherence of "B" and "I":
"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.
- Connect the letters:
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".
- Background patterns:
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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ZZWaang/polyphonic-chord-texture-disentanglement
공식 코드Learning Interpretable Representation for Controllable Polyphonic Music Generation
https://github.com/ZhongliangGuo/LAACA
공식 코드Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack
https://github.com/ZszYmy9/Puff-Net
공식 코드Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network
https://github.com/aaronz345/gtsinger
공식 코드GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks
https://github.com/aaronz345/tcsinger2
공식 코드TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis
https://github.com/abertsch72/perspective-shifting
공식 코드He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues
https://github.com/abhibha1807/robustifying_visual_perception
공식 코드https://github.com/abhinavkashyap/dct
공식 코드So Different Yet So Alike! Constrained Unsupervised Text Style Transfer
https://github.com/adobe-research/domain-expansion
공식 코드HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation
https://github.com/afrozas/skip-thought-gan
공식 코드Generating Text through Adversarial Training using Skip-Thought Vectors
https://github.com/agwabom/pema
공식 코드PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language Models
https://github.com/aigagror/wasserstein-style-transfer
공식 코드https://github.com/aioz-ai/audiodrivenstyletransfer
공식 코드https://github.com/air-discover/lasst
공식 코드https://github.com/airl-iisc/MRFP
공식 코드https://github.com/akashpalrecha/hyperspectral-reconstruction
공식 코드https://github.com/akshaymehra24/riskaversedg
공식 코드On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
https://github.com/alexrodpas/BACON
공식 코드BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer
https://github.com/aliaksandrsiarohin/bae
공식 코드Enhancing Perceptual Attributes with Bayesian Style Generation
https://github.com/aliayub7/EEC
공식 코드EEC: Learning to Encode and Regenerate Images for Continual Learning
https://github.com/alienzhang1996/s2wat
공식 코드S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attention
https://github.com/allambert/torch_itl
공식 코드https://github.com/allenai/tailor
공식 코드Tailor: Generating and Perturbing Text with Semantic Controls
https://github.com/amirdahari1/superres
공식 코드Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks
https://github.com/angelvillar96/STLPose
공식 코드https://github.com/anon-nlp-1234/da-nlg
공식 코드https://github.com/anon294384/diffuseit
공식 코드Diffusion-based Image Translation using Disentangled Style and Content Representation
https://github.com/aramir62/da-nlg
공식 코드https://github.com/architect-road/icongan
공식 코드Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels
https://github.com/arnab39/Semi-supervised-segmentation-cycleGAN
공식 코드https://github.com/artiprocher/sd-webui-fastblend
공식 코드FastBlend: a Powerful Model-Free Toolkit Making Video Stylization Easier
https://github.com/asaakyan/explain-st
공식 코드ICLEF: In-Context Learning with Expert Feedback for Explainable Style Transfer
https://github.com/ashkanpakzad/atn
공식 코드https://github.com/asindel/artfacepoints
공식 코드ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints
https://github.com/askerlee/lapstyle
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.