MLP Mixer – Pytorch An All-MLP solution for Vision, from Google AI, in Pytorch. No convolutions nor attention needed! Install $ pip install mlp-mixer-pytorch Usage import torch from mlp_mixer_pytorch import MLPMixer model = MLPMixer( image_size = 256, patch_size = 16, dim = 512, depth = 12, num_classes = 1000 ) img = torch.randn(1, 3, 256, 256) pred = model(img) # (1, 1000) Citations @misc{tolstikhin2021mlpmixer, title = {MLP-Mixer: An all-MLP Architecture for Vision}, author = {Ilya Tolstikhin and Neil Houlsby and […]

SMPLicit Implementation for the paper SMPLicit: Topology-aware Generative Model for Clothed People (CVPR 2021) Installation Follow these commands to install SMPLicit in your environment. The required libraries are standard, with the possible exception of Kaolin which requires a particular version to run with the current code. git clonehttps://github.com/ecorona/SMPLicit cd SMPLicit Install the dependencies listed in requirements.txt: pip install -r requirements.txt In particular, we use Kaolin v0.1 (see installation command) which should be easy to install. However, if you want to […]

LSNet The location-sensitive visual recognition tasks, including object detection, instance segmentation, and human pose estimation, can be formulated into localizing an anchor point (in red) and a set of landmarks (in green). Our work aims to offer a unified framework for these tasks. Abstract Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks. This paper summarizes these tasks as location-sensitive visual recognition and proposes a unified solution […]

neural-blend-shapes This repository provides an end-to-end library for automatic character rigging, skinning, and blend shapes generation, as well as a visualization tool. It is based on our work Learning Skeletal Articulations with Neural Blend Shapes that is published in SIGGRAPH 2021. Prerequisites Our code has been tested on Ubuntu 18.04. Before starting, please configure your Anaconda environment by conda env create -f environment.yaml conda activate neural-blend-shapes Or you may install the following packages (and their dependencies) manually: pytorch 1.8 tensorboard […]