Code for Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing(ICCV21)

Overview

NeuralGIF

Code for Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing(ICCV21)

We present Neural Generalized Implicit Functions (Neural-GIF), to animate people in clothing as a function of body pose. Neural-GIF learns directly from scans, models complex clothing and produces pose-dependent details for realistic animation. We show for four different characters the query input pose on the left (illustrated with a skeleton) and our output animation on the right.

Dataset and Pretrained models

https://nextcloud.mpi-klsb.mpg.de/index.php/s/FweAP5Js58Q9tsq

Installation

1. Install kaolin: https://github.com/NVIDIAGameWorks/kaolin

2. conda env create -f neuralgif.yml

3. conda activate neuralgif

Training NeuralGIF

 1. Edit configs/*yaml with correct path
        a. data/data_dir:
        b. data/split_file: <path to train/test split file> (see example in dataset folder)
        c. experiment/root_dir: training dir
        d. experiment/exp_name: <exp_name>
 2 . python trainer_shape.py --config=<path to config file>

Generating meshes from NeuralGIF

1. python generator.py --config=<path to config file>

Data preparation

1. SMPL pose and shape parameters:  https://github.com/bharat-b7/IPNet

2. Save the registartion data and original scan data as: 
    
    a. data_dir/scan_dir: contain original scans
    b. data_dir/beta.npy: SMPL beta parameter of subject
    c. data_dir/pose.npz: SMPL pose parameters for all frames of scan

3. Prepare training data:
    python prepare_data/scan_data.py -data_dir=<path to data directory>

Visualisation

python visualisation/render_meshes.py -mesh_path=<folder containing meshes> -out_dir=<output dir>

Citation:

@inproceedings{tiwari21neuralgif,
  title = {Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing},
  author = {Tiwari, Garvita and Sarafianos, Nikolaos and Tung, Tony and Pons-Moll, Gerard},
  booktitle = {International Conference on Computer Vision ({ICCV})},
  month = {October},
  year = {2021},
  }
Owner
Garvita Tiwari
Garvita Tiwari
GLIP: Grounded Language-Image Pre-training

GLIP: Grounded Language-Image Pre-training Updates 12/06/2021: GLIP paper on arxiv https://arxiv.org/abs/2112.03857. Code and Model are under internal

Microsoft 862 Jan 01, 2023
Header-only library for using Keras models in C++.

frugally-deep Use Keras models in C++ with ease Table of contents Introduction Usage Performance Requirements and Installation FAQ Introduction Would

Tobias Hermann 927 Jan 05, 2023
RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP

[Paper] [Π₯Π°Π±Ρ€] [Model Card] [Colab] [Kaggle] RuDOLPH 🦌 πŸŽ„ β˜ƒοΈ One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP Russian Diffusio

AI Forever 232 Jan 04, 2023
A simple, fully convolutional model for real-time instance segmentation.

You Only Look At CoefficienTs β–ˆβ–ˆβ•— β–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β•šβ–ˆβ–ˆβ•— β–ˆβ–ˆβ•”β•β–ˆβ–ˆβ•”β•β•β•β–ˆβ–ˆβ•—β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•—β–ˆβ–ˆβ•”β•β•β•β•β•β•šβ•β•β–ˆβ–ˆβ•”β•β•β• β•šβ–ˆβ–ˆ

Daniel Bolya 4.6k Dec 30, 2022
Bulk2Space is a spatial deconvolution method based on deep learning frameworks

Bulk2Space Spatially resolved single-cell deconvolution of bulk transcriptomes using Bulk2Space Bulk2Space is a spatial deconvolution method based on

Dr. FAN, Xiaohui 60 Dec 27, 2022
Efficiently computes derivatives of numpy code.

Note: Autograd is still being maintained but is no longer actively developed. The main developers (Dougal Maclaurin, David Duvenaud, Matt Johnson, and

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 6.1k Jan 08, 2023
Sleep staging from ECG, assisted with EEG

Sleep_Staging_Knowledge Distillation This codebase implements knowledge distillation approach for ECG based sleep staging assisted by EEG based sleep

2 Dec 12, 2022
Privacy-Preserving Machine Learning (PPML) Tutorial Presented at PyConDE 2022

PPML: Machine Learning on Data you cannot see Repository for the tutorial on Privacy-Preserving Machine Learning (PPML) presented at PyConDE 2022 Abst

Valerio Maggio 10 Aug 16, 2022
ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021

ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021 Dataset Code Demos Authors: He Zhang, Yuting Ye, Tak

HE ZHANG 194 Dec 06, 2022
Official implementation of the NeurIPS 2021 paper Online Learning Of Neural Computations From Sparse Temporal Feedback

Online Learning Of Neural Computations From Sparse Temporal Feedback This repository is the official implementation of the NeurIPS 2021 paper Online L

Lukas Braun 3 Dec 15, 2021
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"

Dataset Distillation by Matching Training Trajectories Project Page | Paper This repo contains code for training expert trajectories and distilling sy

George Cazenavette 256 Jan 05, 2023
[Nature Machine Intelligence' 21] "Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence"

[UCADI] COVID-19 Diagnosis With Federated Learning Intro We developed a Federated Learning (FL) Framework for global researchers to collaboratively tr

HUST EIC AI-LAB 30 Dec 12, 2022
EsViT: Efficient self-supervised Vision Transformers

Efficient Self-Supervised Vision Transformers (EsViT) PyTorch implementation for EsViT, built with two techniques: A multi-stage Transformer architect

Microsoft 352 Dec 25, 2022
Pytorch Geometric Tutorials

Pytorch Geometric Tutorials

Antonio Longa 648 Jan 08, 2023
ICON: Implicit Clothed humans Obtained from Normals

ICON: Implicit Clothed humans Obtained from Normals arXiv, December 2021. Yuliang Xiu Β· Jinlong Yang Β· Dimitrios Tzionas Β· Michael J. Black Table of C

Yuliang Xiu 1.1k Dec 30, 2022
🎯 A comprehensive gradient-free optimization framework written in Python

Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not

Devin Soni 565 Dec 26, 2022
CRNN With PyTorch

CRNN-PyTorch Implementation of https://arxiv.org/abs/1507.05717

Vadim 4 Sep 01, 2022
SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.

The SpeechBrain Toolkit SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch. The goal is to create a single, flexible, and us

SpeechBrain 5.1k Jan 02, 2023
competitions-v2

Codabench (formerly Codalab Competitions v2) Installation $ cp .env_sample .env $ docker-compose up -d $ docker-compose exec django ./manage.py migrat

CodaLab 21 Dec 02, 2022
Production First and Production Ready End-to-End Speech Recognition Toolkit

WeNet δΈ­ζ–‡η‰ˆ Discussions | Docs | Papers | Runtime (x86) | Runtime (android) | Pretrained Models We share neural Net together. The main motivation of WeN

2.7k Jan 04, 2023