Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketches

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Official PyTorch implementation of paper Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketches, presented at CVPR 2021. The code framework is adapted from this CycleGAN repository.

Environments

git clone --recursive https://github.com/bennyguo/sketch2model.git
Python>=3.6
PyTorch>=1.5
install dependencies: pip install -r requirements.txt
build and install Soft Rasterizer: cd SoftRas; python setup.py install

Training

Download shapenet-synthetic.zip here, and extract to load/:

load/
└───shapenet-synthetic /
    │   02691156/
    │   ... ...
    ...

Train on airplane:

python train.py --name exp-airplane --class_id 02691156

You may specify arguments listed in options/base_options.py and options/train_options.py. Saved meshes are named with the corresponding (ground truth or predicted) viewpoints in format e[elevation]a[azimuth]. pred in the filename indicates predicted viewpoint, otherwise the viewpoint is ground truth value (or user-specified when inference, see the Inference section below).

Supported classes:

02691156 Airplane
02828884 Bench
02933112 Cabinet
02958343 Car
03001627 Chair
03211117 Display
03636649 Lamp
03691459 Loudspeaker
04090263 Rifle
04256520 Sofa
04379243 Table
04401088 Telephone
04530566 Watercraft

Evaluation

Test on ShapeNet-Synthetic testset:

python test.py --name [experiment name] --class_id [class id] --test_split test

To test on our ShapeNet-Sketch dataset, you need to first download shapenet-sketch.zip here and extract to load/, then

python test.py --name [experiment name] --class_id [class id] --dataset_mode shapenet_sketch --dataset_root load/shapenet-sketch

About file structures of our ShapeNet-Sketch dataset, please see the dataset definition in data/shapenet_sketch_dataset.py.

Inference

You can generate a 3D mesh from a given sketch image (black background, white strokes) with predicted viewpoint:

python infer.py --name [experiment name] --class_id [class id] --image_path [path/to/sketch]

or with specified viewpoint:

python infer.py --name [experiment name] --class_id [class id] --image_path [path/to/sketch] --view [elevation] [azimuth]

Note that elevation is in range [-20, 40], and azimuth is in range [-180, 180]. We provide some example sketches used in the paper in load/inference.

Pretrained Weights

We provide pretrained weights for all the 13 classes, and weights trained with domain adaptation for 6 of them (airplane, bench, car, chair, rifle, sofa, table). You can download checkpoints.zip here and extract to checkpoints/:

checkpoints/
└───airplane_pretrained/
└───airplane_pretrained+da/
└───... ...

Note that the code and data for domain adaptation are not contained in this repository. You may implement yourself according to the description in the original paper.

Citation

@inproceedings{zhang2021sketch2model,
    title={Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketches},
    author={Zhang, Song-Hai and Guo, Yuan-Chen and Gu, Qing-Wen},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    pages={6012--6021},
    year={2021}
}

Contact

If you have any questions about the implementation or the paper, please feel free to open an issue or contact me at guoyc19@mails.tsinghua.edu.cn.

bennyguo/sketch2model