[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
Riemannian Convex Potential Maps

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited b

Facebook Research 61 Nov 28, 2022
KoCLIP: Korean port of OpenAI CLIP, in Flax

KoCLIP This repository contains code for KoCLIP, a Korean port of OpenAI's CLIP. This project was conducted as part of Hugging Face's Flax/JAX communi

Jake Tae 100 Jan 02, 2023
An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners

An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners This is a coarse version for MAE, only make the pretrain model, the fine

FlyEgle 214 Dec 29, 2022
Mini-hmc-jax - A simple implementation of Hamiltonian Monte Carlo in JAX

mini-hmc-jax This is a simple implementation of Hamiltonian Monte Carlo in JAX t

Martin Marek 6 Mar 03, 2022
CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes

CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes. CHERRY is based on a deep learning model, which consists of a graph convolutional encoder and a link

Kenneth Shang 12 Dec 15, 2022
Code for 'Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning', ICCV 2021

CMIC-Retrieval Code for Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning. ICCV 2021. Introduction In this wo

42 Nov 17, 2022
Bayesian dessert for Lasagne

Gelato Bayesian dessert for Lasagne Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the be

Maxim Kochurov 84 May 11, 2020
PaddleBoBo是基于PaddlePaddle和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目

PaddleBoBo - 元宇宙时代,你也可以动手做一个虚拟主播。 PaddleBoBo是基于飞桨PaddlePaddle深度学习框架和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目。PaddleBoBo致力于简单高效、可复用性强,只需要一张带人像的图片和一段文字,就能

502 Jan 08, 2023
Pytorch implementation of BRECQ, ICLR 2021

BRECQ Pytorch implementation of BRECQ, ICLR 2021 @inproceedings{ li&gong2021brecq, title={BRECQ: Pushing the Limit of Post-Training Quantization by Bl

Yuhang Li 148 Dec 28, 2022
Semi-supevised Semantic Segmentation with High- and Low-level Consistency

Semi-supevised Semantic Segmentation with High- and Low-level Consistency This Pytorch repository contains the code for our work Semi-supervised Seman

123 Dec 30, 2022
Stacked Generative Adversarial Networks

Stacked Generative Adversarial Networks This repository contains code for the paper "Stacked Generative Adversarial Networks", CVPR 2017. Part of the

Xun Huang 241 May 07, 2022
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.

sne4onnx A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or

Katsuya Hyodo 10 Aug 30, 2022
Robot Reinforcement Learning on the Constraint Manifold

Implementation of "Robot Reinforcement Learning on the Constraint Manifold"

31 Dec 05, 2022
Source code for Zalo AI 2021 submission

zalo_ltr_2021 Source code for Zalo AI 2021 submission Solution: Pipeline We use the pipepline in the picture below: Our pipeline is combination of BM2

128 Dec 27, 2022
Code for the published paper : Learning to recognize rare traffic sign

Improving traffic sign recognition by active search This repo contains code for the paper : "Learning to recognise rare traffic signs" How to use this

samsja 4 Jan 05, 2023
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model Baris Gecer 1, Binod Bhattarai 1

Baris Gecer 190 Dec 29, 2022
Interactive Visualization to empower domain experts to align ML model behaviors with their knowledge.

An interactive visualization system designed to helps domain experts responsibly edit Generalized Additive Models (GAMs). For more information, check

InterpretML 83 Jan 04, 2023
PyTorch Implementation of "Light Field Image Super-Resolution with Transformers"

LFT PyTorch implementation of "Light Field Image Super-Resolution with Transformers", arXiv 2021. [pdf]. Contributions: We make the first attempt to a

Squidward 62 Nov 28, 2022
This is a TensorFlow implementation for C2-Rec

This is a TensorFlow implementation for C2-Rec We refer to the repo SASRec. Requirements requirement.txt Datasets This repo includes Amazon Beauty dat

7 Nov 14, 2022
Image restoration with neural networks but without learning.

Warning! The optimization may not converge on some GPUs. We've personally experienced issues on Tesla V100 and P40 GPUs. When running the code, make s

Dmitry Ulyanov 7.4k Jan 01, 2023