https://github.com/hwang9u/hum2Guitar
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Image Generation (synthesis) is the task of generating new images from an existing dataset.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Very Deep Convolutional Networks for Large-Scale Image Recognition
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
Diverse Image-to-Image Translation via Disentangled Representations
Image Inpainting for Irregular Holes Using Partial Convolutions
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis
A Style-Based Generator Architecture for Generative Adversarial Networks
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
SinGAN: Learning a Generative Model from a Single Natural Image
Image-to-Image Translation with Conditional Adversarial Networks
StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Conditional Image Generation and Manipulation for User-Specified Content
ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.