Accelerated SMPL operation, commonly used in generate 3D human mesh, STAR included.

Related tags

Deep LearningSMPL2
Overview

SMPL2

An enchanced and accelerated SMPL operation which commonly used in 3D human mesh generation. It takes a poses, shapes, cam_trans as inputs, outputs a high-dimensional 3D mesh verts.

This packages provides:

  • Highly optimized pytorch acceleration with FP16 infer enabled;
  • Supported ONNX export and infer via ort, so that it might able used into TensorRT or OpenVINO on cpu;
  • Support STAR, next generation of SMPL.
  • Provide commonly used geoemtry built-in support without torchgeometry or kornia.

STAR model download from: https://star.is.tue.mpg.de/downloads

Examples

Some pipelines build with SMPL2 support.

Copyrights

Copyrights belongs to Copyright (C) 2020 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) and Lucas Jin

Owner
JinTian
You know who I am.
JinTian
GitHub Repository
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters

CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt

Paul Gavrikov 18 Dec 30, 2022
Finite-temperature variational Monte Carlo calculation of uniform electron gas using neural canonical transformation.

CoulombGas This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX,

FermiFlow 9 Mar 03, 2022
Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model

Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model. Designed sample dashboard with insights and recommendation for

Yash 2 Apr 07, 2022
Instance-wise Feature Importance in Time (FIT)

Instance-wise Feature Importance in Time (FIT) FIT is a framework for explaining time series perdiction models, by assigning feature importance to eve

Sana 46 Dec 25, 2022
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

DOC | Quick Start | 中文 Breaking News !! 🔥 🔥 🔥 OGB-LSC KDD CUP 2021 winners announced!! (2021.06.17) Super excited to announce our PGL team won TWO

1.5k Jan 06, 2023
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
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation

Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation This is the official repository for our paper Neural Reprojection Error

Hugo Germain 78 Dec 01, 2022
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

117 Dec 28, 2022
A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal

A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases,

Chris Hughes 110 Dec 23, 2022
Character Grounding and Re-Identification in Story of Videos and Text Descriptions

Character in Story Identification Network (CiSIN) This project hosts the code for our paper. Youngjae Yu, Jongseok Kim, Heeseung Yun, Jiwan Chung and

8 Dec 09, 2022
Official implementation of Neural Bellman-Ford Networks (NeurIPS 2021)

NBFNet: Neural Bellman-Ford Networks This is the official codebase of the paper Neural Bellman-Ford Networks: A General Graph Neural Network Framework

MilaGraph 136 Dec 21, 2022
(CVPR2021) Kaleido-BERT: Vision-Language Pre-training on Fashion Domain

Kaleido-BERT: Vision-Language Pre-training on Fashion Domain Mingchen Zhuge*, Dehong Gao*, Deng-Ping Fan#, Linbo Jin, Ben Chen, Haoming Zhou, Minghui

248 Dec 04, 2022
Implementation of EMNLP 2017 Paper "Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog" using PyTorch and ParlAI

Language Emergence in Multi Agent Dialog Code for the Paper Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog Satwik Kottur, José M.

Karan Desai 105 Nov 25, 2022
Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

Shakespeare translations using TensorFlow This is an example of using the new Google's TensorFlow library on monolingual translation going from modern

Motoki Wu 245 Dec 28, 2022
Code for the paper "Curriculum Dropout", ICCV 2017

Curriculum Dropout Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability dis

Pietro Morerio 21 Jan 02, 2022
Official implementation of the paper "Topographic VAEs learn Equivariant Capsules"

Topographic Variational Autoencoder Paper: https://arxiv.org/abs/2109.01394 Getting Started Install requirements with Anaconda: conda env create -f en

T. Andy Keller 69 Dec 12, 2022
Adversarial examples to the new ConvNeXt architecture

Adversarial examples to the new ConvNeXt architecture To get adversarial examples to the ConvNeXt architecture, run the Colab: https://github.com/stan

Stanislav Fort 19 Sep 18, 2022
PyTorch implementation of paper: HPNet: Deep Primitive Segmentation Using Hybrid Representations.

HPNet This repository contains the PyTorch implementation of paper: HPNet: Deep Primitive Segmentation Using Hybrid Representations. Installation The

Siming Yan 42 Dec 07, 2022
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

Yihui He 1k Jan 03, 2023
MDMM - Learning multi-domain multi-modality I2I translation

Multi-Domain Multi-Modality I2I translation Pytorch implementation of multi-modality I2I translation for multi-domains. The project is an extension to

Hsin-Ying Lee 107 Nov 04, 2022