Awesome

Sync2Gen

Code for ICCV 2021 paper: Scene Synthesis via Uncertainty-Driven Attribute Synchronization

0. Environment

Environment: python 3.6 and cuda 10.0 on Ubuntu 18.04

• Pytorch 1.4.0
• tensorflow 1.14.0 (for tensorboard)

1. Dataset

├──dataset_3dfront/
    ├──data
        ├── bedroom
            ├── 0_abs.npy
            ├── 0_rel.pkl
            ├── ...
        ├── living
            ├── 0_abs.npy
            ├── 0_rel.pkl
            ├── ...
        ├── train_bedroom.txt
        ├── train_living.txt
        ├── val_bedroom.txt
        └── val_living.txt

See 3D-FRONT Dataset for dataset generation.

2. VAE

2.1 Generate scenes from random noises

Download the pretrained model from https://drive.google.com/file/d/1VKNlEdUj1RBUOjBaBxE5xQvfsZodVjam/view?usp=sharing

Sync2Gen
└── log
    └── 3dfront
        ├── bedroom
        │   └── vaef_lr0001_w00001_B64
        │       ├── checkpoint_eval799.tar
        │       └── pairs
        └── living
            └── vaef_lr0001_w00001_B64
                ├── checkpoint_eval799.tar
                └── pairs

type='bedroom'; # or living
CUDA_VISIBLE_DEVICES=0 python ./test_sparse.py  --type $type  --log_dir ./log/3dfront/$type/vaef_lr0001_w00001_B64 --model_dict=model_scene_forward --max_parts=80 --num_class=20 --num_each_class=4 --batch_size=32 --variational --latent_dim 20 --abs_dim 16  --weight_kld 0.0001  --learning_rate 0.001 --use_dumped_pairs --dump_results --gen_from_noise --num_gen_from_noise 100

The predictions are dumped in ./dump/$type/vaef_lr0001_w00001_B64

2.2 Training

To train the network:

type='bedroom'; # or living
CUDA_VISIBLE_DEVICES=0 python ./train_sparse.py --data_path ./dataset_3dfront/data  --type $type  --log_dir ./log/3dfront/$type/vaef_lr0001_w00001_B64  --model_dict=model_scene_forward --max_parts=80 --num_class=20 --num_each_class=4 --batch_size=64 --variational --latent_dim 20 --abs_dim 16  --weight_kld 0.0001  --learning_rate 0.001

3. Bayesian optimization

cd optimization

3.1 Prior generation

See Prior generation.

3.2 Optimization

type=bedroom # or living;
bash opt.sh $type vaef_lr0001_w00001_B64  EXP_NAME

We use Pytorch-LBFGS for optimization.

3.3 Visualization

There is a simple visualization tool:

type=bedroom # or living
bash vis.sh $type vaef_lr0001_w00001_B64 EXP_NAME

The visualization is in ./vis. {i:04d}_2(3)d_pred.png is the initial prediction from VAE. {i:04d}_2(3)d_sync.png is the optimized layout after synchronization.

Acknowledgements

The repo is built based on:

• VoteNet
• Pytorch-LBFGS

We thank the authors for their great job.

Contact

If you have any questions, you can contact Haitao Yang (yanghtr [AT] outlook [DOT] com).