⇥The objective is to transfer garments across images of people with arbitrary body pose, shape and clothing. Garment transfer is a challenging task that requires (i) disentangling the features of the clothing from the body pose and shape and (ii) realistic synthesis of the garment texture on the new body.
- Transfer clothes between photos using AI. From a single image!
- Multi-Garment Net: Learning to Dress 3D People from Images
- Morphing architectures for pose-based image generation
- Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On
- SwapNet: Image Based Garment Transfer
- Pose Guided Fashion Image Synthesis Using Deep Generative Model
- Semi-supervised Learning explained
- Weakly Supervised Learning: Introduction and Best Practices
- Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation
- Humans Image Segmentation with Unet using Tensorflow Keras
- Image Segmentation using UNET Tensorflow
- Understanding Semantic Segmentation with UNET
- Image segmentation
- ModaNet: A Large-scale Street Fashion Dataset with Polygon Annotations
- Difference between AlexNet, VGGNet, ResNet, and Inception
- Convolutions and Backpropagations
- Image Segmentation Guide
- Residual Block
- Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras
- RoI pooling in TensorFlow(Per-Trained library)
- Implementing RoI Pooling in TensorFlow + Keras
- Transfer clothes between photos using AI. From a single image!
- Image Based Virtual Try-On Network From Unpaired Data
- Evolution of Object Detection Networks
- U-net for image segmentation
- Large-scale Fashion (DeepFashion) Database
- Fashioninsta Unannotated Dataset
- iMaterialist (Fashion) 2019 at FGVC6
- Clothing Segmentation
- Look Into Person Dataset
- CP Viton Dataset
- Course 1 - Neural Networks and Deep learning
- Course 2 - Improving Deep Neural Networks
- Course 3 - Convolutional Neural Networks
