Skip to content

Latest commit

 

History

History
69 lines (55 loc) · 5.47 KB

README.md

File metadata and controls

69 lines (55 loc) · 5.47 KB
Raw

Learning Generative Models of Textured 3D Meshes from Real-World Images

This is the reference implementation of "Learning Generative Models of Textured 3D Meshes from Real-World Images", accepted at ICCV 2021.

Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurelien Lucchi. Learning Generative Models of Textured 3D Meshes from Real-World Images. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021.

This work is a follow-up of Convolutional Generation of Textured 3D Meshes, in which we learn a GAN for generating 3D triangle meshes and the corresponding texture maps using 2D supervision. In this work, we relax the requirement for keypoints in the pose estimation step, and generalize the approach to unannotated collections of images and new categories/datasets such as ImageNet.

Setup

Instructions on how to set up dependencies, datasets, and pretrained models can be found in SETUP.md

Quick start

In order to test our pretrained models, the minimal setup described in SETUP.md is sufficient. No dataset setup is required. We provide an interface for evaluating FID scores, as well as an interface for exporting a sample of generated 3D meshes (both as a grid of renderings and as .obj meshes).

Exporting a sample

You can export a sample of generated meshes using --export-sample. Here are some examples:

python run_generation.py --name pretrained_imagenet_car_singletpl --dataset imagenet_car --gpu_ids 0 --batch_size 10 --export_sample --how_many 40
python run_generation.py --name pretrained_imagenet_airplane_singletpl --dataset imagenet_airplane --gpu_ids 0 --batch_size 10 --export_sample --how_many 40
python run_generation.py --name pretrained_imagenet_elephant_singletpl --dataset imagenet_elephant --gpu_ids 0 --batch_size 10 --export_sample --how_many 40
python run_generation.py --name pretrained_cub_singletpl --dataset cub --gpu_ids 0 --batch_size 10 --export_sample --how_many 40
python run_generation.py --name pretrained_all_singletpl --dataset all --conditional_class --gpu_ids 0 --batch_size 10 --export_sample --how_many 40

This will generate a sample of 40 meshes, render them from random viewpoints, and export the final result to the output directory as a png image. In addition, the script will export the meshes as .obj files (along with material and texture). These can be imported into Blender or other modeling tools. You can switch between the single-template and multi-template settings by appending either _singletpl or _multitpl to the experiment name.

Evaluating FID on pretrained models

You can evaluate the FID of a model by specifying --evaluate. For the models trained to generate a single category (setting A):

python run_generation.py --name pretrained_cub_singletpl --dataset cub --gpu_ids 0,1,2,3 --batch_size 64 --evaluate
python run_generation.py --name pretrained_p3d_car_singletpl --dataset p3d_car --gpu_ids 0,1,2,3 --batch_size 64 --evaluate
python run_generation.py --name pretrained_imagenet_zebra --dataset imagenet_zebra_singletpl --gpu_ids 0,1,2,3 --batch_size 64 --evaluate

For the conditional models trained to generate all classes (setting B), you can specify the category to evaluate (e.g. motorcycle):

python run_generation.py --name pretrained_all_singletpl --dataset all --conditional_class --gpu_ids 0,1,2,3 --batch_size 64 --evaluate --filter_class motorcycle

As before, you can switch between the single-template and multi-template settings by appending either _singletpl or _multitpl to the experiment name. You can of course also adjust the number of GPUs and batch size to suit your computational resources. For evaluation, 16 elements per GPU is a sensible choice. You can also tune the number of data-loading threads using the --num_workers argument (default: 4 threads). Note that the FID will exhibit a small variance depending on the chosen batch size.

Training

See TRAINING.md for the instructions on how to generate the pseudo-ground-truth dataset and train a new model from scratch. The documentation also provides instructions on how to run the pose estimation steps and run the pipeline from scratch on a custom dataset.

Citation

If you use this work in your research, please consider citing our paper(s):

@inproceedings{pavllo2021textured3dgan,
  title={Learning Generative Models of Textured 3D Meshes from Real-World Images},
  author={Pavllo, Dario and Kohler, Jonas and Hofmann, Thomas and Lucchi, Aurelien},
  booktitle={IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}

@inproceedings{pavllo2020convmesh,
  title={Convolutional Generation of Textured 3D Meshes},
  author={Pavllo, Dario and Spinks, Graham and Hofmann, Thomas and Moens, Marie-Francine and Lucchi, Aurelien},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2020}
}

License and Acknowledgments

Our work is licensed under the MIT license. For more details, see LICENSE. This repository builds upon convmesh and includes third-party libraries which may be subject to their respective licenses: Synchronized-BatchNorm-PyTorch, the data loader from CMR, and FID evaluation code from pytorch-fid.