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/KunZhou9646/controllable_evc_code
Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset
https://github.com/Kunhao-Liu/StyleGaussian
StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting
https://github.com/Kyubyong/expressive_tacotron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/Larryliu912/Vincent-2.0
https://github.com/LaurentBerger/neuralstyle
https://github.com/LeMU-Haruka/reconet-mindspore
https://github.com/LearningHx/StyA2K
https://github.com/Lees001/FastPhotoStyle
https://github.com/Leinadh/avatar-image-generator
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/LeslieZhoa/DCT-NET.Pytorch
DCT-Net: Domain-Calibrated Translation for Portrait Stylization
https://github.com/LiangHann/USAR
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects
https://github.com/Lornatang/PyTorch-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/LouieYang/deep-photo-styletransfer-tf
https://github.com/LouieYang/stroke-controllable-fast-style-transfer
Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
https://github.com/Lu-Hsuan/A-Neural-Algorithm-of-Artistic-Style
https://github.com/LucasSheng/avatar-net
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
https://github.com/LynnHo/CycleGAN-Tensorflow-2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/LynnHo/CycleGAN-Tensorflow-PyTorch-Simple
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/MQSchleich/SatelliteFAMOS
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
https://github.com/MQSchleich/SatelliteGAN
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
https://github.com/Manan1811/Neural-Style-Transfer
https://github.com/ManthanBhala/Style-Transfer-by-Rigid-Alignment
Style Transfer by Rigid Alignment in Neural Net Feature Space
https://github.com/MarvinLavechin/imagetranslation-tensorflow
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/Maxencephilbert/In_Silico_Labeling_CBIO_internship
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Unpaired_Image_to_Image_Translation_using_Cycle_Consistent_Adversarial_Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Meghanshu-Bhatt/Neural-artistic-style-transfer
https://github.com/MehmetBarutcu/PHM-Paper-Implementation
https://github.com/Mind23-2/MindCode-8
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/MindSpore-paper-code-2/code3/tree/main/StyTr-2
https://github.com/MindSpore-paper-code-3/code9/tree/main/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/MindSpore-scientific-2/code-10/tree/main/VAE-Creative-Discovery-using-QD-Search
VAE based Text Style Transfer with Pivot Words Enhancement Learning
https://github.com/MindSpore-scientific-2/code-10/tree/main/ZoneoutRNN
VAE based Text Style Transfer with Pivot Words Enhancement Learning
https://github.com/MindSpore-scientific-2/code-11/tree/main/pixelshuffle1d
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/MindSpore-scientific-2/code-4/tree/main/MicAugment
https://github.com/MindSpore-scientific-2/code-4/tree/main/pixelshuffle1d
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.