https://github.com/robbiebarrat/sculpture-gan
3D-DCGAN trained on a corpus of 3D printable objects - as a result, the generations are usually 3D printable
https://github.com/robbiebarrat/sculpture-gan
Last synced: 13 days ago
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3D-DCGAN trained on a corpus of 3D printable objects - as a result, the generations are usually 3D printable
- Host: GitHub
- URL: https://github.com/robbiebarrat/sculpture-gan
- Owner: robbiebarrat
- Created: 2017-09-12T18:10:59.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-10-29T00:11:15.000Z (over 7 years ago)
- Last Synced: 2025-03-29T16:51:15.608Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 1.12 MB
- Stars: 112
- Watchers: 7
- Forks: 27
- Open Issues: 2
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Metadata Files:
- Readme: README.md
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README
# Sculpture-GAN
3D DCGAN inspired GAN trained on 32x32x32 voxelizations of [Thingi10k](https://ten-thousand-models.appspot.com/) - a corpus of 10,000 3D printable objects and sculptures - as a result, the generated sculptures are almost always 3D-printable, but usually do not have any real 'meaning', and are just abstract sorts of shapes.
I originally began this project as an attempt at generative architecture; but lack of an appropriate dataset held me back from that. Currently working with trying to get something usable from the google sketchup 3d workshop
# Example abstract shape generations
## 3D-printed generation
## Computer visualized generations
# Usage
There are really only 3 files you need to use to generate your own 3D shapes.
## generate_dataset.py
You need to download [Thingi10k](https://ten-thousand-models.appspot.com/), and in line 8 of generate_dataset.py, set the variable `path_to_dataset` to be the path to your `raw_meshes` folder from your thingi10k download.
Next, just run the file with `python generate_dataset.py`, and it will start generating numpy arrays that contain all of the now-voxelized models in thingi10k and save them to your `data/` folder.
## train.py
On line 23 of `train.py`, point it to the desired (usually the largest) .npy file in your `data/` folder by setting `numpy_array_saved` equal to its path.
Then, you can just run `python train.py` and trained network checkpoints will start to populate your `network_checkpoints/` folder.
## visualize.py
Point `visualize.py` to an array of generations in your `generations` folder (an array is created every 50 epochs) by setting `array_to_visualize` equal to its path.
This script will allow you to see the models that network has generated in 3D, with the option to save as PNG, obj, etc.
# Issues / Future efforts
Right now, the main issue is the fact that it's very hard to get data for this sort of project; the only two datasets I have encountered are shapnet and thingi10k; currently; i am trying to train a conditional version of the 3d GAN with shapenet so that you can select that you want to generate a table, chair, etc, and the network will move past just generating "shapes"...