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/astra-vision/poda
공식 코드https://github.com/auspicious3000/SpeechSplit
공식 코드Unsupervised Speech Decomposition via Triple Information Bottleneck
https://github.com/awei669/VQ-Font
공식 코드https://github.com/azadis/MC-GAN
공식 코드https://github.com/bandainamcoresearchinc/bandai-namco-research-motiondataset
공식 코드Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization
https://github.com/bardhprenkaj/gist
공식 코드https://github.com/barisbatuhan/dass_det_inference
공식 코드Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings
https://github.com/barisbatuhan/dass_detector
공식 코드Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings
https://github.com/barisgecer/facegan
공식 코드https://github.com/behavioral-data/PARTNER
공식 코드https://github.com/bgalerne/scaling_painting_style_transfer
공식 코드https://github.com/birdortyedi/cifr-pytorch
공식 코드Patch-wise Contrastive Style Learning for Instagram Filter Removal
https://github.com/birdortyedi/denim
공식 코드Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction
https://github.com/birdortyedi/instagram-filter-removal-pytorch
공식 코드https://github.com/boeun-kim/most
공식 코드MoST: Motion Style Transformer between Diverse Action Contents
https://github.com/boschresearch/issa
공식 코드Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization
https://github.com/bottlecapper/EmoMUNIT
공식 코드https://github.com/bovifocr/tcdiff
공식 코드TCDiff: Triple Condition Diffusion Model with 3D Constraints for Stylizing Synthetic Faces
https://github.com/byungsook/neural-flow-style
공식 코드https://github.com/cc-hpc-itwm/facialgan
공식 코드FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces
https://github.com/cesaremagnetti/automaticusnavigation
공식 코드Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer
https://github.com/chahuja/mix-stage
공식 코드Style Transfer for Co-Speech Gesture Animation: A Multi-Speaker Conditional-Mixture Approach
https://github.com/chandagrover/sem-cs
공식 코드Sem-CS: Semantic CLIPStyler for Text-Based Image Style Transfer
https://github.com/chaofengc/Face-Sketch
공식 코드Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
https://github.com/chaofengc/PSFRGAN
공식 코드Progressive Semantic-Aware Style Transformation for Blind Face Restoration
https://github.com/chendaryen/artfusion
공식 코드ArtFusion: Controllable Arbitrary Style Transfer using Dual Conditional Latent Diffusion Models
https://github.com/chenyingshu/advances_3d_neural_stylization
공식 코드https://github.com/chiutaiyin/PhotoWCT2
공식 코드https://github.com/chiutaiyin/pca-knowledge-distillation
공식 코드https://github.com/chuanli11/MGANs
공식 코드Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
https://github.com/chxy95/genlv
공식 코드Learning A Low-Level Vision Generalist via Visual Task Prompt
https://github.com/cifkao/ismir2019-music-style-translation
공식 코드Supervised Symbolic Music Style Translation Using Synthetic Data
https://github.com/cifkao/ss-vq-vae
공식 코드https://github.com/cindydeng1991/Wavelet-Domain-Style-Transfer-for-an-Effective-Perception-distortion-Tradeoff-in-Single-Image-Super-
공식 코드https://github.com/cindyxinyiwang/deep-latent-sequence-model
공식 코드A Probabilistic Formulation of Unsupervised Text Style Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.