GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images (NeurIPS 2022)
_{Official PyTorch implementation}

This is GET3D implementation with multi-nodes training support.

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News

• 2022-10-13: Pretrained model on Shapenet released! Check more details here
• 2022-09-29: Code released!
• 2022-09-22: Code will be uploaded next week!

Requirements

• We recommend Linux for performance and compatibility reasons.
• 1 – 8 high-end NVIDIA GPUs. We have done all testing and development using V100 or A100 GPUs.
• 64-bit Python 3.8 and PyTorch 1.9.0. See https://pytorch.org for PyTorch install instructions.
• CUDA toolkit 11.1 or later. (Why is a separate CUDA toolkit installation required? We use the custom CUDA extensions from the StyleGAN3 repo. Please see Troubleshooting) .
• We also recommend to install Nvdiffrast following instructions from official repo, and install Kaolin.
• We provide a script to install packages.

Server usage through Docker

• Build Docker image

cd docker
chmod +x make_image.sh
./make_image.sh get3d:v1

• Start an interactive docker container: docker run --gpus device=all -it --rm -v YOUR_LOCAL_FOLDER:MOUNT_FOLDER -it get3d:v1 bash

Preparing datasets

GET3D is trained on synthetic dataset. We provide rendering scripts for Shapenet. Please refer to readme to download shapenet dataset and render it.

Train the model

Clone the gitlab code and necessary files:

cd YOUR_CODE_PARH
git clone [email protected]:nv-tlabs/GET3D.git
cd GET3D; mkdir cache; cd cache
wget https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl

Train the model

cd YOUR_CODE_PATH 
export PYTHONPATH=$PWD:$PYTHONPATH
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

• Train on the unified generator on cars, motorbikes or chair (Improved generator in Appendix):

python train_3d.py --outdir=PATH_TO_LOG --data=PATH_TO_RENDER_IMG --camera_path PATH_TO_RENDER_CAMERA --gpus=8 --batch=32 --gamma=40 --data_camera_mode shapenet_car  --dmtet_scale 1.0  --use_shapenet_split 1  --one_3d_generator 1  --fp32 0
python train_3d.py --outdir=PATH_TO_LOG --data=PATH_TO_RENDER_IMG --camera_path PATH_TO_RENDER_CAMERA --gpus=8 --batch=32 --gamma=80 --data_camera_mode shapenet_motorbike  --dmtet_scale 1.0  --use_shapenet_split 1  --one_3d_generator 1  --fp32 0
python train_3d.py --outdir=PATH_TO_LOG --data=PATH_TO_RENDER_IMG --camera_path PATH_TO_RENDER_CAMERA --gpus=8 --batch=32 --gamma=400 --data_camera_mode shapenet_chair  --dmtet_scale 0.8  --use_shapenet_split 1  --one_3d_generator 1  --fp32 0

• If want to train on seperate generators (main Figure in the paper):

python train_3d.py --outdir=PATH_TO_LOG --data=PATH_TO_RENDER_IMG --camera_path PATH_TO_RENDER_CAMERA --gpus=8 --batch=32 --gamma=40 --data_camera_mode shapenet_car  --dmtet_scale 1.0  --use_shapenet_split 1  --one_3d_generator 0
python train_3d.py --outdir=PATH_TO_LOG --data=PATH_TO_RENDER_IMG --camera_path PATH_TO_RENDER_CAMERA --gpus=8 --batch=32 --gamma=80 --data_camera_mode shapenet_motorbike  --dmtet_scale 1.0  --use_shapenet_split 1  --one_3d_generator 0
python train_3d.py --outdir=PATH_TO_LOG --data=PATH_TO_RENDER_IMG --camera_path PATH_TO_RENDER_CAMERA --gpus=8 --batch=32 --gamma=3200 --data_camera_mode shapenet_chair  --dmtet_scale 0.8  --use_shapenet_split 1  --one_3d_generator 0

If want to debug the model first, reduce the number of gpus to 1 and batch size to 4 via:

--gpus=1 --batch=4

Inference

Inference on a pretrained model for visualization

• Download pretrained model from here.
• Inference could operate on a single GPU with 16 GB memory.

python train_3d.py --outdir=save_inference_results/shapenet_car  --gpus=1 --batch=4 --gamma=40 --data_camera_mode shapenet_car  --dmtet_scale 1.0  --use_shapenet_split 1  --one_3d_generator 1  --fp32 0 --inference_vis 1 --resume_pretrain MODEL_PATH
python train_3d.py --outdir=save_inference_results/shapenet_chair  --gpus=1 --batch=4 --gamma=40 --data_camera_mode shapenet_chair  --dmtet_scale 0.8  --use_shapenet_split 1  --one_3d_generator 1  --fp32 0 --inference_vis 1 --resume_pretrain MODEL_PATH
python train_3d.py --outdir=save_inference_results/shapenet_motorbike  --gpus=1 --batch=4 --gamma=40 --data_camera_mode shapenet_motorbike  --dmtet_scale 1.0  --use_shapenet_split 1  --one_3d_generator 1  --fp32 0 --inference_vis 1 --resume_pretrain MODEL_PATH

• To generate mesh with textures, add one option to the inference command: --inference_to_generate_textured_mesh 1

• To generate the results with latent code interpolation, add one option to the inference command: --inference_save_interpolation 1

Evluation metrics

Compute FID

• To evaluate the model with FID metric, add one option to the inference command: --inference_compute_fid 1

Compute COV & MMD scores for LFD & CD

• First generate 3D objects for evaluation, add one option to the inference command: --inference_generate_geo 1
• Following README to compute metrics.

License

Copyright © 2022, NVIDIA Corporation & affiliates. All rights reserved.

This work is made available under the Nvidia Source Code License .

Broader Information

GET3D builds upon several previous works:

• Learning Deformable Tetrahedral Meshes for 3D Reconstruction (NeurIPS 2020)
• Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis (NeurIPS 2021)
• Extracting Triangular 3D Models, Materials, and Lighting From Images (CVPR 2022)
• DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer (NeurIPS 2021)
• Nvdiffrast – Modular Primitives for High-Performance Differentiable Rendering (SIGRAPH Asia 2020)

Citation

@inproceedings{gao2022get3d,
title={GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images},
author={Jun Gao and Tianchang Shen and Zian Wang and Wenzheng Chen and Kangxue Yin
and Daiqing Li and Or Litany and Zan Gojcic and Sanja Fidler},
booktitle={Advances In Neural Information Processing Systems},
year={2022}
}