Deep Interpretable Non-Rigid Structure from Motion

This repository contains code for the deep interpretable Non-Rigid Structure from Motion (NRSfM) method.

Paper: https://arxiv.org/abs/1902.10840

Prerequisites

We implement our neural network using python3 with these additional packages:

• TensorFlow for NN building, training and evaluation.
• pandas for result analysis.
• plotly for quick visualization.

You might want to setup Jupyter Notebook for future visualization.

Guide

Our current release has been tested on Ubuntu 16.04.5 LTS.

To get the code, in terminal, run:

git clone https://github.com/kongchen1992/deep-nrsfm.git

Data

We use CMU Graphics Lab Motion Capture Database to train and test our neural network. We create TFRecords for convenience of training and evaluation. You can download the TFRecords file of subject 23 (http://www.cs.cmu.edu/~chenk/deep-nrsfm/23.train) for demo.

Pre-trained models

We offer a pre-trained model for subject 23 for the purpose of demo. You can download it from (http://www.cs.cmu.edu/~chenk/deep-nrsfm/23.tar.gz), unzip and save for demo.

Evaluate pre-trained models

We first change three paths:

• path['tfrecords'] in file motion_capture.py: your path to TFRecords.
• root in file evaluate.sh: your path to model checkpoints i.e. the directory of downloaded pre-trained model.
• output in file evaluate.sh: your path to the result directory.

After changing the paths, simply run in terminal:

./evaluate.sh

This script will run the provided model with provided TFRecords and save errors into CSV file and predictions into NPZ file. You should be able to see 23.csv and 23.npz in your result directory.

Visualize predictions

We offer a Jupyter notebook file for you to quickly check quantitative and qualitative results after evaluation. Start Jupyter by running in terminal:

jupyter notebook

and then open the provided Visualization.ipynb in web UI. After properly setting the path to your evaluations and running the page, you will be able to see the evaluation results and visualizations. An example is demonstrated here (http://www.cs.cmu.edu/~chenk/deep-nrsfm/Visualization.html).

Train a new model

We also offer a script train.sh for you to train a new model. After changing

• path['tfrecords'] in file motion_capture.py: your path to TFRecords.
• root in file train.sh: your path to desired model directory.

You are able to train your own model by simply running in terminal:

./train.sh

Reference

If you use the code, please cite:

@misc{1902.10840,
  Author = {Chen Kong and Simon Lucey},
  Title = {Deep Interpretable Non-Rigid Structure from Motion},
  Year = {2019},
  Eprint = {arXiv:1902.10840},
}

For any questions, please contact Chen Kong (chenk@cs.cmu.edu).