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/manuelruder/artistic-videos
공식 코드https://github.com/manuelruder/fast-artistic-videos
공식 코드https://github.com/manugar/imagestyletransfer
공식 코드https://github.com/mariodoebler/test-time-adaptation
공식 코드Introducing Intermediate Domains for Effective Self-Training during Test-Time
https://github.com/martiansideofthemoon/style-transfer-paraphrase
공식 코드Reformulating Unsupervised Style Transfer as Paraphrase Generation
https://github.com/masud99r/thinker
공식 코드https://github.com/matthias-wright/art-fid
공식 코드https://github.com/mazurowski-lab/breastMRI_styletransfer
공식 코드Deep Learning for Breast MRI Style Transfer with Limited Training Data
https://github.com/mchong6/JoJoGAN
공식 코드https://github.com/medicl-vu/crossmoda2023
공식 코드Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation
https://github.com/megvii-research/megactor
공식 코드MegActor: Harness the Power of Raw Video for Vivid Portrait Animation
https://github.com/megvii-research/megfaceanimate
공식 코드MegActor: Harness the Power of Raw Video for Vivid Portrait Animation
https://github.com/menyifang/dct-net
공식 코드DCT-Net: Domain-Calibrated Translation for Portrait Stylization
https://github.com/michelecafagna26/CHANGE-IT
공식 코드On the interaction of automatic evaluation and task framing in headline style transfer
https://github.com/michiyasunaga/bifi
공식 코드https://github.com/mikasenghaas/sillystill
공식 코드https://github.com/milesigel/audio-sentiment-transfer
공식 코드https://github.com/mindspore-lab/mindediting
공식 코드Tunable Convolutions with Parametric Multi-Loss Optimization
https://github.com/mingkaid/rl-prompt
공식 코드RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning
https://github.com/mingsun-tse/collaborative-distillation
공식 코드Collaborative Distillation for Ultra-Resolution Universal Style Transfer
https://github.com/mingzi151/interpretationdata
공식 코드It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data
https://github.com/mireshghallah/style-pooling
공식 코드Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness
https://github.com/monkeyseedocg/momo-code
공식 코드Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer
https://github.com/mrzzy2021/styledmotionsynthesis
공식 코드Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models
https://github.com/msracver/Style-Feature-Reshuffle
공식 코드https://github.com/multimedialabsfu/learned-point-cloud-compression-for-classification
공식 코드https://github.com/multimodal-art-projection/yue
공식 코드YuE: Scaling Open Foundation Models for Long-Form Music Generation
https://github.com/nadavc220/semanticsegmentationinartpaintings
공식 코드https://github.com/nadeemlab/deepliif
공식 코드https://github.com/naver-ai/Visual-Style-Prompting
공식 코드https://github.com/nazmul-karim170/Free-Editor
공식 코드https://github.com/nazmul-karim170/save-tex2video
공식 코드SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing
https://github.com/nbalepur/text-fact-transfer
공식 코드https://github.com/nchinaev/MobileFace
공식 코드MobileFace: 3D Face Reconstruction with Efficient CNN Regression
https://github.com/neuralchen/ASMAGAN
공식 코드Anisotropic Stroke Control for Multiple Artists Style Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.