This repository contains the Pytorch implementation of Towards Practical Sketch-Based 3D Shape Generation.
You can find detailed usage instructions for training and evaluation below.
If you use our code or dataset, please cite our work:
@ARTICLE{sketch3d2020,
author={Zhong, Yue and Qi, Yonggang and Gryaditskaya, Yulia and Zhang, Honggang and Song, Yi-Zhe},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={Towards Practical Sketch-Based 3D Shape Generation: The Role of Professional Sketches},
year={2021},
volume={31},
number={9},
pages={3518-3528},
doi={10.1109/TCSVT.2020.3040900}
}
First you have to make sure that you have all dependencies in place. The simplest way to do so, is to use anaconda. sss Please refer the README file in each sub-task for detailed instruction. s
Download dataset is easy. Directly download from Dataset with code: fhp7.
Most of our experiments are conducted on the modelsfrom a chair category of the ShapeNetCore V2. We selected these categories guided by the next principles: 1) Easy to sketch. 2) Generality. 3) View differentiability. 4) Shape genius higher than 1. 5) Large inter-category variance. We generate three categories with distinctive styles, whichwe refer to as naive, stylized and style-unified. Please refer paper for further details.
We show an improved performance of deep image modeling.
