https://github.com/CXH-Research/FilmNet
공식 코드A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement
Papers with Code 아카이브
Image Stylization
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Image stylization is a task that involves transforming an input image into a new image that has a different style, while preserving the content of the original image. The goal of image stylization is to create visually appealing images with a specific style or aesthetic, such as impressionism, cubism, or surrealism. It can also be used to make images more visually appealing for specific applications, such as social media or advertising.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DmitryUlyanov/texture_nets
공식 코드Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/GT-RIPL/DomainGeneralization-Stylization
공식 코드Frustratingly Simple Domain Generalization via Image Stylization
https://github.com/MartinBuessemeyer/Artistic-Texture-Control
공식 코드Controlling Geometric Abstraction and Texture for Artistic Images
https://github.com/MaxReimann/WISE-Editing
공식 코드https://github.com/MaxReimann/video-stream-consistency
공식 코드Interactive Control over Temporal Consistency while Stylizing Video Streams
https://github.com/NVIDIA/FastPhotoStyle
공식 코드https://github.com/ZZUTK/SRNTT
공식 코드https://github.com/chxy95/genlv
공식 코드Learning A Low-Level Vision Generalist via Visual Task Prompt
https://github.com/haha-lisa/Diffstyler
공식 코드DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
https://github.com/hansam95/osasis
공식 코드https://github.com/iglict/stylizednerf
공식 코드StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning
https://github.com/kwanyun/LeGO_code
공식 코드https://github.com/lishaoxu1994/instant-nerf-stylization
공식 코드https://github.com/lsx0101/Instant-NeRF-Stylization
공식 코드https://github.com/luo-lorry/Stylized-Medical-Segmentation
공식 코드Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation
https://github.com/mchong6/JoJoGAN
공식 코드https://github.com/salesforce/edict
공식 코드EDICT: Exact Diffusion Inversion via Coupled Transformations
https://github.com/showlab/omniconsistency
공식 코드OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data
https://github.com/songrise/Artist
공식 코드DiffArtist: Towards Structure and Appearance Controllable Image Stylization
https://github.com/toby-katakuri/lp_styletransfernet
공식 코드Multiscale style transfer based on a Laplacian pyramid for traditional Chinese painting
https://github.com/ut-vision/FgSty-CPL
공식 코드https://github.com/yangxy/pasd
공식 코드Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
https://github.com/zalandoresearch/famos
공식 코드Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
https://github.com/zichongc/doesfs
공식 코드Deformable One-shot Face Stylization via DINO Semantic Guidance
https://github.com/zkx06111/rediffusion
공식 코드ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
https://github.com/zyxelsa/cast_pytorch
공식 코드Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
https://github.com/Archangel212/psgan-batik
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
https://github.com/Chad4545/styletransfer
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/CompVis/adaptive-style-transfer
https://github.com/GuillaumeAI/rwml__adaptive_style_transfer
https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/Lees001/FastPhotoStyle
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/MarvinLavechin/imagetranslation-tensorflow
Instance Normalization: The Missing Ingredient for Fast Stylization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.