/SceneMover

Project of Siggraph Asia 2020 paper: Scene Mover: Automatic Move Planning for Scene Arrangement by Deep Reinforcement Learning

Primary LanguagePythonMIT LicenseMIT

SceneMover

This repository is the implementation of our SIGGRAPH Asia 2020 paper:

Scene Mover: Automatic Move Planning for Scene Arrangement by Deep Reinforcement Learning

Hanqing Wang, Wei Liang, Lap-Fai Yu.

Introduction

We propose a novel approach for automatically generating a move plan for scene arrangement. Given a scene like an apartment with many furniture objects, to transform its layout into another layout, one would need to determine a collision-free move plan.

Please refer to our paper for the detailed formulations.

Demonstration

Click here to watch the demonstration on Youtube. Scene Mover

Environment Installation

  1. Install Requirements

    • python 3.6.9
    • g++ 5.4.0
    • CUDA 10.1
    • pillow 6.1.0
    • tensorflow 1.14.0
    • tensorboardx 1.8

  2. Install Jupyter Install jupyter using the following scripts. pip install jupyter

  3. Build Files

  cd src/utils
  g++ -shared -O2 search.cpp --std=c++11 -ldl -fPIC -o search.so

Q-Net Training

To be updated.

Inference:

To be updated.

Contributors

To be updated.

TODO

  • Release the checkpoint.
  • Add training code.

Citation

Please cite this paper in your publications if it helps your research:

@article{wang2020scenem,
  author = {Hanqing Wang and Wei Liang and Lap-Fai Yu},
  title = {Scene Mover: Automatic Move Planning for Scene Arrangement by Deep Reinforcement Learning}, 
  journal = {ACM Transactions on Graphics},
  volume = {39},
  number = {6},
  year = {2020}
}

License

Scene Mover is freely available for non-commercial use, and may be redistributed under these conditions. Please see the license for further details. For commercial license, please contact the authors.

Contact Information

  • hanqingwang[at]bit[dot]edu[dot]cn, Hanqing Wang