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/ucsd-dsc-arts/dsc160-final-dsc160-final-group6
https://github.com/ucsd-dsc-arts/dsc160-final-dsc160_final_group4
https://github.com/ucsd-dsc-arts/dsc160-final-group17_final
https://github.com/ucsd-ml-arts/ml-art-final-chandhini-g-1
https://github.com/ucsd-ml-arts/ml-art-final-jeffrey
https://github.com/ucsd-ml-arts/ml-art-final-justin-law
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/umd-fire-coml/2020-Image-Stylization
https://github.com/ushashwat/Neural-Style-Transfer
https://github.com/ushashwat/neural_style_transfer
https://github.com/uzielroy/CUT
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/vanhuyz/CycleGAN-TensorFlow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/vedpatel49/Neural-Style-Transfer
https://github.com/verbeemen/south-park-avatar-generator
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/vg-sravan/Cycle-Consistent-Generative-Adversarial-Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/victespinoza/NeuralStyleTransfer
https://github.com/vidit2011998/neuralstyletransfer
https://github.com/vieduy/Neural-Style-Transfer
https://github.com/vijini/FM-GAN
https://github.com/vijishmadhavan/ArtLine
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/SkinDeep
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/Toon-Me
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vincent-thevenin/Realistic-Neural-Talking-Head-Models
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/vinoth654321/Beta-Vae-face-dataset
https://github.com/viriditass/Style-transfer
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/wandb/awesome-dl-projects
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
https://github.com/wandb/gallery
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
https://github.com/wangguanzhi/LADN
LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
https://github.com/wangyePHD/OmniStyle
OmniStyle: Filtering High Quality Style Transfer Data at Scale
https://github.com/wasserstein-transfer/wasserstein-transfer.github.io
https://github.com/webis-de/acl-24
https://github.com/weihaox/UMBRAE
Brain Captioning: Decoding human brain activity into images and text
https://github.com/wilile26811249/Style_Transfer_PyTorch
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/winfried-loetzsch/wise
Style Transfer by Relaxed Optimal Transport and Self-Similarity
https://github.com/wkhademi/ImageEnhancement
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/wkostuch/wild-style
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.