PyTorch implementation for paper AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis, by Zhiqin Chen, Kangxue Yin, and Sanja Fidler.
Paper | Video | Project page
The code has been tested on Ubuntu. The required packages are listed in requirements.yml. We recommend using conda to set up the environment; simply run the following scripts.
conda env create --file requirements.yml
conda activate auvnet
Run this script to build cython modules.
python setup.py build_ext --inplace
Please see data_preparation folder.
Note: the code only uses the first 80% of the shapes for training.
If you want to train on all the shapes, add --use_all_data to all the commands in the .sh files.
Change data_dir in the .sh files before running the following scripts.
To train AUV-Net on the car category of ShapeNet, run the following script.
sh script/train_car.sh
To obtain aligned texture images, run the following script.
sh script/test_car.sh
The aligned texture images and their meshes with uv coordinates are written to folder aligned_textures.
Simply swap the aligned texture images of two meshes.
Run the following script to quickly obtain an 8x8 table of geometry-texture hybrid shapes written in folder hybrid.
python utilities/demo_texture_transfer.py
If you find our work useful in your research, please consider citing:
@inproceedings{chen2022AUVNET,
title = {AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis},
author = {Zhiqin Chen and Kangxue Yin and Sanja Fidler},
booktitle = {The Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}
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This work is made available under the Nvidia Source Code License .