A collection of papers I am interested in.

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

Codes for CyGen, the novel generative modeling framework proposed in "On the Generative Utility of Cyclic Conditionals" (NeurIPS-21)

Time-Lapse Disentanglement With Conditional GANs [SIGGRAPH 2022]

[CVPR 2021] Adversarial Generation of Continuous Images

[CVPR 2022] StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2

Official Implementation for "Transferring Unconditional to Conditional GANs with Hyper-Modulation" CVPRW 22 https://arxiv.org/abs/2112.02219

🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

[NeurIPS 2022] (Amortized) distributional control for pre-trained generative models

This repo is for our paper "ControlVAE: Controllable Variational Autoencoder" published at ICML 2020. It can be used for text generation, image generation

code for "Isolating Sources of Disentanglement in Variational Autoencoders".

[ICML'23] StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

pix2pix3D: Generating 3D Objects from 2D User Inputs

[ICCV 2023] StyleGANEX: StyleGAN-Based Manipulation Beyond Cropped Aligned Faces

Implementation of GigaGAN, new SOTA GAN out of Adobe. Culmination of nearly a decade of research into GANs

Official Code for DragGAN (SIGGRAPH 2023)

[CVPR 2024] Official implementation of FreeDrag: Feature Dragging for Reliable Point-based Image Editing

MagicEdit: High-Fidelity Temporally Coherent Video Editing