# Diffusion-based Virtual Try-On Methods

- [StableVITON : Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On](https://arxiv.org/pdf/2312.01725)
- [IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild](https://arxiv.org/pdf/2403.05139)
- [DCI-VTON : Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow](https://arxiv.org/pdf/2308.06101)
- [LaDI-VTON : Latent Diffusion Textual-Inversion Enhanced Virtual Try-On](https://arxiv.org/pdf/2305.13501)
- [OOTDiffusion : Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on](https://arxiv.org/pdf/2403.01779)

# Components Used in OOTDiffusion

## OpenPose
- [Research Paper : Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://arxiv.org/pdf/1812.08008)
- [Openpose Paper Summary](https://towardsdatascience.com/openpose-research-paper-summary-realtime-multi-person-2d-pose-estimation-3563a4d7e66)

## U-Net
- [Video Summary](https://youtu.be/NhdzGfB1q74?si=6SsScwlFgJUBmKU1)
- [Research Paper : U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597)
- [UNet Architecture Explained (Blog)](https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47)

## VAE (Variational Autoencoder)
- [Video Summary - CodeEmporium](https://youtu.be/fcvYpzHmhvA?si=Gw1sT3wqzgdmVc0B)
- [Video Summary - Arxiv Insights](https://youtu.be/9zKuYvjFFS8?si=u4AJIM8PUtAUzh5B)
- [Blog Post - Understanding Variational Autoencoders](https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73)
- [Maths behind the VAE](https://lilianweng.github.io/posts/2018-08-12-vae/)

## Attention Mechanism
- [Attention Explained - StatQuest](https://youtu.be/PSs6nxngL6k?si=qMLZq8OTv8mZuJQj)
- [Cross Attention Mech](https://youtu.be/aw3H-wPuRcw?si=BZpAN_qRXnNrjlcn)

## Dropout Regularization
- [Neural Dropout](https://towardsdatascience.com/dropout-in-neural-networks-47a162d621d9)