SceneFormer: Indoor Scene Generation with Transformers
Initial code release for the Sceneformer paper, contains models, train and test scripts for the shape conditioned model. Text conditioned model and detailed README coming soon.
Please also check the project website here
Setup
Install the requirements in requirements.txt
and environment.yaml
in a conda environment. Packages that are common can be installed either through pip or conda.
Prepare Data
The SUNCG dataset is currently not available, hence all related files have been removed. The dataset can be prepared with the scripts which were taken from deepsynth.
Train
Configure the experiment in configs/scene_shift_X_config.yaml
where X
is one of cat, dim, loc, ori
Then run
python scene_scripts/train_shift_X_lt.py configs/scene_shift_X_config.yaml
to train the model X
.
Test
Configure the model paths in scene_scripts/test.py
and then run
python scene_scripts/test.py
If you find our work useful, please consider citing us:
@article{wang2020sceneformer,
title={SceneFormer: Indoor Scene Generation with Transformers},
author={Wang, Xinpeng and Yeshwanth, Chandan and Nie{\ss}ner, Matthias},
journal={arXiv preprint arXiv:2012.09793},
year={2020}
}