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/laihuiyuan/Pre-trained-formality-transfer
공식 코드Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer
https://github.com/laihuiyuan/eval-formality-transfer
공식 코드Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer
https://github.com/laihuiyuan/generic-resources-for-tst
공식 코드https://github.com/laihuiyuan/multilingual-tst
공식 코드Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer
https://github.com/lancopku/Augmented_Data_for_FST
공식 코드https://github.com/lbsswu/Chinese_style_transfer
공식 코드Neural Abstract Style Transfer for Chinese Traditional Painting
https://github.com/ldzhangyx/vis2mus
공식 코드Vis2Mus: Exploring Multimodal Representation Mapping for Controllable Music Generation
https://github.com/leongatys/NeuralImageSynthesis
공식 코드https://github.com/lephuoccat/Supervised-Encoding-Quantizer
공식 코드https://github.com/lijuncen/Sentiment-and-Style-Transfer
공식 코드Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
https://github.com/limingcv/Photorealistic-Style-Transfer
공식 코드https://github.com/linear95/rlm
공식 코드https://github.com/linfengWen98/CAP-VSTNet
공식 코드CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer
https://github.com/linllll/maskst
공식 코드https://github.com/linnanwang/AlphaX-NASBench101
공식 코드AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
https://github.com/lishaoxu1994/diffstyler
공식 코드https://github.com/lishaoxu1994/instant-nerf-stylization
공식 코드https://github.com/litterbrother-xiao/overview-of-non-autoregressive-applications
공식 코드A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond
https://github.com/liusongxiang/StarGAN-Voice-Conversion
공식 코드AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/lkeab/gaussian-grouping
공식 코드https://github.com/lkpengcs/dcda
공식 코드https://github.com/llnl/luar
공식 코드Can Authorship Representation Learning Capture Stylistic Features?
https://github.com/lsx0101/Instant-NeRF-Stylization
공식 코드https://github.com/lu-m13/OptimalStyleTransfer
공식 코드https://github.com/luanfujun/deep-photo-styletransfer
공식 코드https://github.com/lukasHoel/stylemesh
공식 코드StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions
https://github.com/luo-lorry/Stylized-Medical-Segmentation
공식 코드Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation
https://github.com/lvyiwei1/StylePTB
공식 코드StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer
https://github.com/lvyiwei1/diffuseq_styleptb
공식 코드Fine-grained Text Style Transfer with Diffusion-Based Language Models
https://github.com/machelreid/editpro
공식 코드https://github.com/machelreid/lewis
공식 코드LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer
https://github.com/maciej3031/comixify
공식 코드https://github.com/magenta/magenta/blob/master/magenta/models/arbitrary_image_stylization/README.md
공식 코드Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/mahmoudnafifi/color-aware-style-transfer
공식 코드https://github.com/majedelhelou/VIDIT
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.