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/kaminyou/urust
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/kanishk16/Image-Style-Transfer
https://github.com/karl-hajjar/Generative-Adversarial-Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/karuj/StyleTransfer
https://github.com/keonlee9420/Comprehensive-Tacotron2
https://github.com/keonlee9420/Cross-Speaker-Emotion-Transfer
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/khornlund/CycleGAN-Music-Style-Transfer-1
https://github.com/khush3/GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/kidach1/NeuralArtisticStyle
https://github.com/kirill-ionkin/NST_Gatys
https://github.com/knurpsbram/autovc_wavenetvocoder_griffinlim_experiments
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/kramer99/neural-style-transfer
https://github.com/ksivaman/super-res
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ku2482/vae.pytorch
https://github.com/kumnikhil/christmAIs_replica
https://github.com/kvsnoufal/neural_style_transfer
https://github.com/kynk94/TF2-Image-Generation
https://github.com/kyuer/language-style-transfer
https://github.com/kzkymur/transition
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/labmlai/annotated_deep_learning_paper_implementations
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/laughtervv/tf_neural_renderer
https://github.com/lcosmo/LIMP
LIMP: Learning Latent Shape Representations with Metric Preservation Priors
https://github.com/lcosmo/isospectralization
Isospectralization, or how to hear shape, style, and correspondence
https://github.com/leehomyc/cyclegan-1
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/lengstrom/fast-style-transfer
https://github.com/leolle/StyleTransfer
https://github.com/leonardoperu/Monetmaker
https://github.com/leongatys/PytorchNeuralStyleTransfer
https://github.com/lhannest/neuralstyle
https://github.com/liamheng/pytorch1.4-wct
https://github.com/libreai/neural-painters-x
https://github.com/lihengbit/Pytorch1.4-WCT
https://github.com/lingofunk/lingofunk-transfer-style
https://github.com/lit-leo/inner-convex-nn
https://github.com/liulai/reconet-torch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.