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SceneMover

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

Install / Use

/learn @HanqingWangAI/SceneMover
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

SceneMover

<div align="center"> <img src="assets/teaser.png", width="700"> </div>

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

<!-- <div align="center"> <img src="imgs/case.gif", width="700"> </div> -->

Click here to watch the demonstration on Youtube. Scene Mover

<!-- <iframe width="560" height="315" src="https://www.youtube.com/embed/zSM-s7zh-vk" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> -->

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.

<!-- 1. Download the checkpoint of the agent to directory `snap/agent/state_dict/best_val_unseen`, the checkpoint of the speaker to directory `snap/speaker/state_dict/best_val_unseen_bleu`. 2. Start a Jupyter service and run the jupyter notebook [evaluation.ipynb](evaluation.ipynb). -->

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
View on GitHub
GitHub Stars98
CategoryEducation
Updated9d ago
Forks15

Languages

Python

Security Score

100/100

Audited on Mar 12, 2026

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