Implementation for the paper SMPLicit: Topology-aware Generative Model for Clothed People (CVPR 2021)

Related tags

Deep LearningSMPLicit
Overview

SMPLicit: Topology-aware Generative Model for Clothed People

[Project] [arXiv]

License

Software Copyright License for non-commercial scientific research purposes. Please read carefully the terms and conditions and any accompanying documentation before you download and/or use the SMPLicit model, data and software, (the "Model & Software"), including 3D meshes, blend weights, blend shapes, textures, software, scripts, and animations. By downloading and/or using the Model & Software (including downloading, cloning, installing, and any other use of this github repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Model & Software. Any infringement of the terms of this agreement will automatically terminate your rights under this License.

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 clone https://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 use a later version, you might need to update the import to TriangleMesh in SMPLicit/SMPLicit.py

  • Download the SMPL model from here and place it in SMPLicit/utils/

To be able to import and use SMPLicit in another project, just use run python setup.py install in the main folder.

Usage

To check that everything is going well, run one of the test scripts under the examples folder. The first example will just show a simple T-Shirt on a standard shaped SMPL and visualize it using trimesh, to make sure everything is working.

cd examples/
python example.py

SMPLicit can represent clothes of different types, so the following example will also add lower-body clothes, hair and shoes into the example:

python example_fullbody.py

And finally one can interpolate between clothes of different types. For instance, moving between a jacket, tops, short or long sleeved T-Shirts. The following script will generate object meshes that represent these clothes and will be saved in interpolation/, below the main folder.

python interpolate.py

Citation

If you find the code useful, please cite:

@inproceedings{corona2021smplicit,
    Author = {Enric Corona and Albert Pumarola and Guillem Aleny{\`a} and Pons-Moll, Gerard and Moreno-Noguer, Francesc},
    Title = {SMPLicit: Topology-aware Generative Model for Clothed People},
    Year = {2021},
    booktitle = {CVPR},
}
MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

MemStream Implementation of MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift . Siddharth Bhatia, Arjit Jain, Shivi

Stream-AD 61 Dec 02, 2022
Image Segmentation Animation using Quadtree concepts.

QuadTree Image Segmentation Animation using QuadTree concepts. Usage usage: quad.py [-h] [-fps FPS] [-i ITERATIONS] [-ws WRITESTART] [-b] [-img] [-s S

Alex Eidt 29 Dec 25, 2022
A `Neural = Symbolic` framework for sound and complete weighted real-value logic

Logical Neural Networks LNNs are a novel Neuro = symbolic framework designed to seamlessly provide key properties of both neural nets (learning) and s

International Business Machines 138 Dec 19, 2022
Ludwig Benchmarking Toolkit

Ludwig Benchmarking Toolkit The Ludwig Benchmarking Toolkit is a personalized benchmarking toolkit for running end-to-end benchmark studies across an

HazyResearch 17 Nov 18, 2022
This repository contains python code necessary to replicated the experiments performed in our paper "Invariant Ancestry Search"

InvariantAncestrySearch This repository contains python code necessary to replicated the experiments performed in our paper "Invariant Ancestry Search

Phillip Bredahl Mogensen 0 Feb 02, 2022
Simple streamlit app to demonstrate HERE Tour Planning

Table of Contents About the Project Built With Getting Started Prerequisites Installation Usage Roadmap Contributing License Acknowledgements About Th

Amol 8 Sep 05, 2022
Swapping face using Face Mesh with TensorFlow Lite

Swapping face using Face Mesh with TensorFlow Lite

iwatake 17 Apr 26, 2022
这是一个利用facenet和retinaface实现人脸识别的库,可以进行在线的人脸识别。

Facenet+Retinaface:人脸识别模型在Keras当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络,分别是retinaface和fa

Bubbliiiing 31 Nov 15, 2022
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing da

MIT CSAIL Computer Vision 4.5k Jan 08, 2023
[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

Shihua Huang 23 Jul 22, 2022
Chunkmogrify: Real image inversion via Segments

Chunkmogrify: Real image inversion via Segments Teaser video with live editing sessions can be found here This code demonstrates the ideas discussed i

David Futschik 112 Jan 04, 2023
Picasso: A CUDA-based Library for Deep Learning over 3D Meshes

The Picasso Library is intended for complex real-world applications with large-scale surfaces, while it also performs impressively on the small-scale applications over synthetic shape manifolds. We h

97 Dec 01, 2022
Implementation of paper "Towards a Unified View of Parameter-Efficient Transfer Learning"

A Unified Framework for Parameter-Efficient Transfer Learning This is the official implementation of the paper: Towards a Unified View of Parameter-Ef

Junxian He 216 Dec 29, 2022
Portfolio asset allocation strategies: from Markowitz to RNNs

Portfolio asset allocation strategies: from Markowitz to RNNs Research project to explore different approaches for optimal portfolio allocation starti

Luigi Filippo Chiara 1 Feb 05, 2022
Production First and Production Ready End-to-End Speech Recognition Toolkit

WeNet 中文版 Discussions | Docs | Papers | Runtime (x86) | Runtime (android) | Pretrained Models We share neural Net together. The main motivation of WeN

2.7k Jan 04, 2023
Free like Freedom

This is all very much a work in progress! More to come! ( We're working on it though! Stay tuned!) Installation Open an Anaconda Prompt (in Windows, o

2.3k Jan 04, 2023
[ICCV'21] Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

CKDN The official implementation of the ICCV2021 paper "Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment" O

Multimedia Research 50 Dec 13, 2022
Evaluation framework for testing segmentation networks in PyTorch

Evaluation framework for testing segmentation networks in PyTorch. What segmentation network to choose for next Kaggle competition? This benchmark knows the answer!

Eugene Khvedchenya 37 Apr 27, 2022
Kindle is an easy model build package for PyTorch.

Kindle is an easy model build package for PyTorch. Building a deep learning model became so simple that almost all model can be made by copy and paste from other existing model codes. So why code? wh

Jongkuk Lim 77 Nov 11, 2022
Flexible time series feature extraction & processing

tsflex is a toolkit for flexible time series processing & feature extraction, that is efficient and makes few assumptions about sequence data. Useful

PreDiCT.IDLab 206 Dec 28, 2022