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/cmry/style-obfuscation
공식 코드https://github.com/comp-syn/comp-syn
공식 코드Divergences in Color Perception between Deep Neural Networks and Humans
https://github.com/computer-vision2022/pama
공식 코드https://github.com/cookielee77/DAST
공식 코드https://github.com/crosszamirski/guided-i2i
공식 코드Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels
https://github.com/crowddynamicslab/styleinfusion
공식 코드Audience-Centric Natural Language Generation via Style Infusion
https://github.com/cxa9264/confeti
공식 코드https://github.com/cyclomon/clipstyler
공식 코드CLIPstyler: Image Style Transfer with a Single Text Condition
https://github.com/czczup/URST
공식 코드Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
https://github.com/czczup/block-shuffle
공식 코드Block Shuffle: A Method for High-resolution Fast Style Transfer with Limited Memory
https://github.com/dada-bots/dadaGP
공식 코드DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
https://github.com/damo-vilab/composer
공식 코드Composer: Creative and Controllable Image Synthesis with Composable Conditions
https://github.com/danifranco/em_domain_adaptation
공식 코드Deep learning based domain adaptation for mitochondria segmentation on EM volumes
https://github.com/danruta/neat
공식 코드https://github.com/dapangliu/lamer
공식 코드Non-Parallel Text Style Transfer with Self-Parallel Supervision
https://github.com/davidmhart/interpolated-selectionconv
공식 코드Interpolated SelectionConv for Spherical Images and Surfaces
https://github.com/dayihengliu/Fine-Grained-Style-Transfer
공식 코드Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
https://github.com/decurtoidiaz/ce4
공식 코드Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4
https://github.com/dengpingfan/fsgan
공식 코드https://github.com/devavrattomar/sst
공식 코드Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
https://github.com/dingsheng-ong/ipr-gan
공식 코드Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
https://github.com/diyiiyiii/StyTR-2
공식 코드https://github.com/dkadish/Style-Transfer-for-Object-Detection-in-Art
공식 코드Improving Object Detection in Art Images Using Only Style Transfer
https://github.com/dongqi-fan/irconstyle
공식 코드IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer
https://github.com/drscotthawley/SPNet
공식 코드ConvNets for Counting: Object Detection of Transient Phenomena in Steelpan Drums
https://github.com/duyuwen-duen/roco-sim
공식 코드RoCo-Sim: Enhancing Roadside Collaborative Perception through Foreground Simulation
https://github.com/dvs-whu/E-SAI
공식 코드Event-based Synthetic Aperture Imaging with a Hybrid Network
https://github.com/dykang/PASTEL
공식 코드https://github.com/echigot/cactif
공식 코드Style Transfer with Diffusion Models for Synthetic-to-Real Domain Adaptation
https://github.com/eezkni/fdl
공식 코드Misalignment-Robust Frequency Distribution Loss for Image Transformation
https://github.com/elbria/xformal-fost-meta
공식 코드https://github.com/emmanueliarussi/generative3DSpongiosa
공식 코드Generative Modelling of 3D in-silico Spongiosa with Controllable Micro-Structural Parameters
https://github.com/emreaksan/deepwriting
공식 코드DeepWriting: Making Digital Ink Editable via Deep Generative Modeling
https://github.com/endywon/aesust
공식 코드https://github.com/endywon/microast
공식 코드MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.