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/hanqi-qi/matte
공식 코드https://github.com/hansam95/osasis
공식 코드https://github.com/hanwen0529/DSCSI-GAN
공식 코드Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning
https://github.com/haoheliu/AudioLDM
공식 코드AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
https://github.com/haoningwu3639/StoryGen
공식 코드Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models
https://github.com/happylittlecat2333/Auffusion
공식 코드Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation
https://github.com/haryoa/stif-indonesia
공식 코드https://github.com/hayeong0/DDDM-VC
공식 코드https://github.com/hayeong0/Diff-HierVC
공식 코드https://github.com/hila-chefer/targetclip
공식 코드https://github.com/hkust-vgd/architectural_style_transfer
공식 코드Time-of-Day Neural Style Transfer for Architectural Photographs
https://github.com/hnuzhy/ssda-yolo
공식 코드SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection
https://github.com/hoenigpeter/randomized_texturing
공식 코드https://github.com/hologerry/Attr2Font
공식 코드https://github.com/hongyehu/RG-Flow
공식 코드RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
https://github.com/horsepurve/StyleSegor
공식 코드Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
https://github.com/huage001/adaattn
공식 코드AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
https://github.com/huage001/dynast
공식 코드DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
https://github.com/huage001/painttransformer
공식 코드Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
https://github.com/huage001/transfer-any-style
공식 코드Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate
https://github.com/hubertsgithub/style_painting_robustness
공식 코드What Can Style Transfer and Paintings Do For Model Robustness?
https://github.com/hyeonseob-nam/Batch-Instance-Normalization
공식 코드Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
https://github.com/i2-multimedia-lab/diffusest
공식 코드DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfer
https://github.com/iamalexkorotin/Wasserstein2GenerativeNetworks
공식 코드https://github.com/ianxmason/local-phases
공식 코드https://github.com/iglict/stylizednerf
공식 코드StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning
https://github.com/iiscleap/zest
공식 코드Zero Shot Audio to Audio Emotion Transfer With Speaker Disentanglement
https://github.com/iit-pavis/learnaug-uda
공식 코드Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation
https://github.com/ilyagusev/rudetox
공식 코드https://github.com/instantx-research/instantstyle-plus
공식 코드InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation
https://github.com/isi-nlp/astrapop
공식 코드https://github.com/isi-nlp/stamp
공식 코드https://github.com/isrugeek/semcolour
공식 코드Nanoscale Microscopy Images Colorization Using Neural Networks
https://github.com/itsss/img2img_sa
공식 코드Unsupervised Image-to-Image Translation with Self-Attention Networks
https://github.com/ivanmontero/autobot
공식 코드Sentence Bottleneck Autoencoders from Transformer Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.