https://github.com/xchenz/invertavatar_page
공식 코드InvertAvatar: Incremental GAN Inversion for Generalized Head Avatars
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xdxie/WAS_WordArt-Segmentation
공식 코드https://github.com/xfsun99/ccyclegan-tf2
공식 코드https://github.com/xh-liu/CC-FPSE
공식 코드Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
https://github.com/xh-liu/Open-Edit
공식 코드Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
https://github.com/xhu248/cisfa_da
공식 코드https://github.com/xhu248/cond_ddpm_wsss
공식 코드Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation
https://github.com/xialeiliu/GFR-IL
공식 코드https://github.com/xianfengwu01/lightgen
공식 코드https://github.com/xiangchenyin/grpose
공식 코드GRPose: Learning Graph Relations for Human Image Generation with Pose Priors
https://github.com/xianggao1102/fbsdiff
공식 코드https://github.com/xianggao1102/fcdiffusion
공식 코드Frequency-Controlled Diffusion Model for Versatile Text-Guided Image-to-Image Translation
https://github.com/xianggao1102/learning-to-incorporate-texture-saliency-adaptive-attention-to-image-cartoonization
공식 코드Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
https://github.com/xiangyu-mm/unifashion
공식 코드UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation
https://github.com/xiaoachen98/DDB
공식 코드Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
https://github.com/xiaohu-zheng/deep-mc-qr
공식 코드https://github.com/xiaoxiaxusummer/meg_dt
공식 코드Mobile Edge Generation-Enabled Digital Twin: Architecture Design and Research Opportunities
https://github.com/xiechun298/sv-drr
공식 코드SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
https://github.com/xiefan-guo/ctsdg
공식 코드Image Inpainting via Conditional Texture and Structure Dual Generation
https://github.com/xiexing0916/ARRA
공식 코드https://github.com/xiezhy6/pasta-gan
공식 코드Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN
https://github.com/ximinng/inversion-by-inversion
공식 코드https://github.com/xinario/awesome-gan-for-medical-imaging
공식 코드https://github.com/xingangpan/shadegan
공식 코드A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
https://github.com/xingchengxu/bsde-gen
공식 코드Deep Generative Modeling with Backward Stochastic Differential Equations
https://github.com/xingy038/dreamer-xl
공식 코드Dreamer XL: Towards High-Resolution Text-to-3D Generation via Trajectory Score Matching
https://github.com/xingyu-zheng/bidm
공식 코드BiDM: Pushing the Limit of Quantization for Diffusion Models
https://github.com/xingzhehe/ganseg
공식 코드GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation
https://github.com/xinwangchen/edt
공식 코드EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching
https://github.com/xinweizhang/daebm
공식 코드Persistently Trained, Diffusion-assisted Energy-based Models
https://github.com/xinychen/transdim
공식 코드Laplacian Convolutional Representation for Traffic Time Series Imputation
https://github.com/xjqicuhk/SIMS
공식 코드https://github.com/xjtu-xgu/kpg-rl
공식 코드https://github.com/xjtu-xgu/otcs
공식 코드Optimal Transport-Guided Conditional Score-Based Diffusion Models
https://github.com/xmed-lab/geca
공식 코드https://github.com/xmindflow/INCODE
공식 코드INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.