EigenGAN TensorFlow implementation of EigenGAN: Layer-Wise Eigen-Learning for GANs EigenGAN Usage Environment Python 3.6 TensorFlow 1.15 OpenCV, scikit-image, tqdm, oyaml we recommend Anaconda or Miniconda, then you can create the environment with commands below conda create -n EigenGAN python=3.6 source activate EigenGAN conda install opencv scikit-image tqdm tensorflow-gpu=1.15 conda install -c conda-forge oyaml NOTICE: if you create a new conda environment, remember to activate it before any other command source activate EigenGAN Data Preparation CelebA-unaligned (10.2GB, higher quality than the […]

TCMR_RELEASE This repository is the official Pytorch implementation of Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video. The base codes are largely borrowed from VIBE. Qualtitative result Paper teaser video Installation TCMR is tested on Ubuntu 16.04 with Pytorch 1.4 and Python 3.7.10. You may need sudo privilege for the installation. source scripts/install_pip.sh Quick demo Download the pre-trained demo TCMR and required data by below command and download SMPL layers from here (male&female) and […]

TorchShard TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards. It can reduce GPU memory and scale up the training when the model has massive linear layers (e.g., ViT, BERT and GPT) or huge classes (millions). It has the same API design as PyTorch. Installation pip install torchshard More options in INSTALL.md. Usage import torchshard as ts ts.init_process_group(group_size=2) # init parallel groups m = torch.nn.Sequential( torch.nn.Linear(20, 30, bias=True), ts.nn.ParallelLinear(30, 30, bias=True, dim=None), # equal to nn.Linear() […]

LED2-Net This is PyTorch implementation of our CVPR 2021 Oral paper “LED2-Net: Monocular 360˚ Layout Estimation via Differentiable Depth Rendering”. You can visit our project website and upload your own panorama to see the 3D results! Prerequisite This repo is primarily based on PyTorch. You can use the follwoing command to intall the dependencies. pip install -r requirements.txt Preparing Training Data Under LED2Net/Dataset, we provide the dataloader of Matterport3D and Realtor360. The annotation formats of the two datasets follows PanoAnnotator. […]