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/t04glovern/neural-painters
Neural Painters: A learned differentiable constraint for generating brushstroke paintings
https://github.com/tahasamavati/Cycle-GAN-Tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/taivu1998/GANime
https://github.com/takaahii/chainer-gogh
https://github.com/taki0112/Batch_Instance_Normalization-Tensorflow
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
https://github.com/taki0112/GDWCT-Tensorflow
Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation
https://github.com/tanjeffreyz/neural-algorithm-of-artistic-style
https://github.com/tarujg/domain-adapt
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/taylorjocelyn/diffusion-model-quantization
https://github.com/tbullmann/imagetranslation-tensorflow
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/tcmxx/CNTKUnityTools
https://github.com/telecombcn-dl/2018-dlai-team5
https://github.com/tensorflow/models/tree/master/research/gan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/tensorpack/tensorpack/tree/master/examples/GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/tfonta/ca2sis
https://github.com/tfonta/semantic-vae
https://github.com/thatbrguy/Dehaze-GAN
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/the-super-toys/glimpse-models
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/theinmate4587/Art-Generation
https://github.com/thiagoambiel/NeuralAtlases
https://github.com/thinkingmachines/christmAIs
https://github.com/tiijana/Neural-Style-Transfer-
https://github.com/timonziegenbein/inappropriateness-mitigation
https://github.com/timy90022/VEGAN
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects
https://github.com/timy90022/WEGAN
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects
https://github.com/tmabraham/UPIT
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/tobwei/disentintel
Unsupervised Speech Decomposition via Triple Information Bottleneck
https://github.com/tollymune/CycleGAN-PyTorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/tomgillooly/geogan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/tpark94/speedplusbaseline
Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft
https://github.com/tpark94/spnv2
Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft
https://github.com/ttecles/aidl-lyrics-recognition
End-to-end lyrics Recognition with Voice to Singing Style Transfer
https://github.com/tyui592/Avatar-Net_Pytorch
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
https://github.com/tyui592/adain_pytorch
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/u7javed/Bidirectional-Image-to-Image-Translator
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.