[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Overview

Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Official implementation for Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints.

You would need different python environments to finish each step, please check the corresponding README.md file in each folder. You should also follow the instructions in those files to set up dataset path.

Please see the project page for more details.

NOTE

We use OpenPose (version 1.2) to get 2D pose (Body_25 model), please follow their instructions to install it.

Example

For video demo/dance.mp4, you can follow these steps to get our motion reconstruction result. But you might need to modify some lines to specify the path.

  • Check openpose folder: Use OpenPose to get per-frame detection json files. You need to modify some lines to specify the path. I have also put the raw OpenPose results in demo/dance.
  • Check ground_detector folder: We need to convert json files to npy format. See op2npy.py.
  • Check ground_detector folder: Get ground contact detection results. See inference.py.
  • Check motion_reconstruct folder: We need to convert json files to h5 format. See run_openpose.py.
  • Check motion_reconstruct folder: Last step, see refine_video.py

I have also put all the intermediate result files in the corresponding path, you should be able to run the last step directly (if you have specify the path correctly).

TODO

  • (Probably after ECCV submission) Code clean up for motion reconstruction part

Citation

If you find this code useful for your research, please consider citing the following paper:

@inproceedings{zou2020reducing,
    author    = {Zou, Yuliang and Yang, Jimei and Ceylan, Duygu and Zhang, Jianming and Perazzi, Federico and Huang, Jia-Bin}, 
    title     = {Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints}, 
    booktitle = {Winter Conference on Applications of Computer Vision},
    year      = {2020}
}
Owner
Virginia Tech Vision and Learning Lab
Virginia Tech Vision and Learning Lab
Code for "Unsupervised Source Separation via Bayesian inference in the latent domain"

LQVAE-separation Code for "Unsupervised Source Separation via Bayesian inference in the latent domain" Paper Samples GT Compressed Separated Drums GT

Michele Mancusi 30 Oct 25, 2022
Implementation for Simple Spectral Graph Convolution in ICLR 2021

Simple Spectral Graph Convolutional Overview This repo contains an example implementation of the Simple Spectral Graph Convolutional (S^2GC) model. Th

allenhaozhu 64 Dec 31, 2022
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021, official Pytorch implementatio

Microsoft 247 Dec 25, 2022
The openspoor package is intended to allow easy transformation between different geographical and topological systems commonly used in Dutch Railway

Openspoor The openspoor package is intended to allow easy transformation between different geographical and topological systems commonly used in Dutch

7 Aug 22, 2022
PyTorch code for the paper: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning This is the PyTorch implementation of our paper: FeatMatch: Feature-Based Augmentat

43 Nov 19, 2022
CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

CALVIN CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks Oier Mees, Lukas Hermann, Erick Rosete,

Oier Mees 107 Dec 26, 2022
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023
SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.

SOLO: Segmenting Objects by Locations This project hosts the code for implementing the SOLO algorithms for instance segmentation. SOLO: Segmenting Obj

Xinlong Wang 1.5k Dec 31, 2022
Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

AimCLR This is an official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Reco

Gty 44 Dec 17, 2022
NeurIPS'21 Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows

NeurIPS'21 Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows This repo contains the code for the paper Tractable Densit

Layer6 Labs 4 Dec 12, 2022
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

YeongHyeon Park 7 Aug 28, 2022
RoFormer_pytorch

PyTorch RoFormer 原版Tensorflow权重(https://github.com/ZhuiyiTechnology/roformer) chinese_roformer_L-12_H-768_A-12.zip (提取码:xy9x) 已经转化为PyTorch权重 chinese_r

yujun 283 Dec 12, 2022
Exporter for Storage Area Network (SAN)

SAN Exporter Prometheus exporter for Storage Area Network (SAN). We all know that each SAN Storage vendor has their own glossary of terms, health/perf

vCloud 32 Dec 16, 2022
This repository is for EMNLP 2021 paper: It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data

InterpretationData This repository is for our EMNLP 2021 paper: It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpr

4 Apr 21, 2022
Pretraining Representations For Data-Efficient Reinforcement Learning

Pretraining Representations For Data-Efficient Reinforcement Learning Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Ch

Mila 40 Dec 11, 2022
Using Machine Learning to Create High-Res Fine Art

BIG.art: Using Machine Learning to Create High-Res Fine Art How to use GLIDE and BSRGAN to create ultra-high-resolution paintings with fine details By

Robert A. Gonsalves 13 Nov 27, 2022
AI Virtual Calculator: This is a simple virtual calculator based on Artificial intelligence.

AI Virtual Calculator: This is a simple virtual calculator that works with gestures using OpenCV. We will use our hand in the air to click on the calc

Md. Rakibul Islam 1 Jan 13, 2022
CLIP + VQGAN / PixelDraw

clipit Yet Another VQGAN-CLIP Codebase This started as a fork of @nerdyrodent's VQGAN-CLIP code which was based on the notebooks of @RiversWithWings a

dribnet 276 Dec 12, 2022
Simple-System-Convert--C--F - Simple System Convert With Python

Simple-System-Convert--C--F REQUIREMENTS Python version : 3 HOW TO USE Run the c

Jonathan Santos 2 Feb 16, 2022
Custom studies about block sparse attention.

Block Sparse Attention 研究总结 本人近半年来对Block Sparse Attention(块稀疏注意力)的研究总结(持续更新中)。按时间顺序,主要分为如下三部分: PyTorch 自定义 CUDA 算子——以矩阵乘法为例 基于 Triton 的 Block Sparse A

Chen Kai 2 Jan 09, 2022