Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"

Related tags

Deep Learninggraf
Overview

GRAF


This repository contains official code for the paper GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis.

You can find detailed usage instructions for training your own models and using pre-trained models below.

If you find our code or paper useful, please consider citing

@inproceedings{Schwarz2020NEURIPS,
  title = {GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis},
  author = {Schwarz, Katja and Liao, Yiyi and Niemeyer, Michael and Geiger, Andreas},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020}
}

Installation

First you have to make sure that you have all dependencies in place. The simplest way to do so, is to use anaconda.

You can create an anaconda environment called graf using

conda env create -f environment.yml
conda activate graf

Next, for nerf-pytorch install torchsearchsorted. Note that this requires torch>=1.4.0 and CUDA >= v10.1. You can install torchsearchsorted via

cd submodules/nerf_pytorch
pip install -r requirements.txt
cd torchsearchsorted
pip install .
cd ../../../

Demo

You can now test our code via:

python eval.py configs/carla.yaml --pretrained --rotation_elevation

This script should create a folder results/carla_128_from_pretrained/eval/ where you can find generated videos varying camera pose for the Cars dataset.

Datasets

If you only want to generate images using our pretrained models you do not need to download the datasets. The datasets are only needed if you want to train a model from scratch.

Cars

To download the Cars dataset from the paper simply run

cd data
./download_carla.sh
cd ..

This creates a folder data/carla/ downloads the images as a zip file and extracts them to data/carla/. While we do not use camera poses in this project we provide them for completeness. Your can download them by running

cd data
./download_carla_poses.sh
cd ..

This downloads the camera intrinsics (single file, equal for all images) and extrinsics corresponding to each image.

Faces

Download celebA. Then replace data/celebA in configs/celebA.yaml with *PATH/TO/CELEBA*/Img/img_align_celebA.

Download celebA_hq. Then replace data/celebA_hq in configs/celebAHQ.yaml with *PATH/TO/CELEBA_HQ*.

Cats

Download the CatDataset. Run

cd data
python preprocess_cats.py PATH/TO/CATS/DATASET
cd ..

to preprocess the data and save it to data/cats. If successful this script should print: Preprocessed 9407 images.

Birds

Download CUB-200-2011 and the corresponding Segmentation Masks. Run

cd data
python preprocess_cub.py PATH/TO/CUB-200-2011 PATH/TO/SEGMENTATION/MASKS
cd ..

to preprocess the data and save it to data/cub. If successful this script should print: Preprocessed 8444 images.

Usage

When you have installed all dependencies, you are ready to run our pre-trained models for 3D-aware image synthesis.

Generate images using a pretrained model

To evaluate a pretrained model, run

python eval.py CONFIG.yaml --pretrained --fid_kid --rotation_elevation --shape_appearance

where you replace CONFIG.yaml with one of the config files in ./configs.

This script should create a folder results/EXPNAME/eval with FID and KID scores in fid_kid.csv, videos for rotation and elevation in the respective folders and an interpolation for shape and appearance, shape_appearance.png.

Note that some pretrained models are available for different image sizes which you can choose by setting data:imsize in the config file to one of the following values:

configs/carla.yaml: 
    data:imsize 64 or 128 or 256 or 512
configs/celebA.yaml:
    data:imsize 64 or 128
configs/celebAHQ.yaml:
    data:imsize 256 or 512

Train a model from scratch

To train a 3D-aware generative model from scratch run

python train.py CONFIG.yaml

where you replace CONFIG.yaml with your config file. The easiest way is to use one of the existing config files in the ./configs directory which correspond to the experiments presented in the paper. Note that this will train the model from scratch and will not resume training for a pretrained model.

You can monitor on http://localhost:6006 the training process using tensorboard:

cd OUTPUT_DIR
tensorboard --logdir ./monitoring --port 6006

where you replace OUTPUT_DIR with the respective output directory.

For available training options, please take a look at configs/default.yaml.

Evaluation of a new model

For evaluation of the models run

python eval.py CONFIG.yaml --fid_kid --rotation_elevation --shape_appearance

where you replace CONFIG.yaml with your config file.

Multi-View Consistency Check

You can evaluate the multi-view consistency of the generated images by running a Multi-View-Stereo (MVS) algorithm on the generated images. This evaluation uses COLMAP and make sure that you have COLMAP installed to run

python eval.py CONFIG.yaml --reconstruction

where you replace CONFIG.yaml with your config file. You can also evaluate our pretrained models via:

python eval.py configs/carla.yaml --pretrained --reconstruction

This script should create a folder results/EXPNAME/eval/reconstruction/ where you can find generated multi-view images in images/ and the corresponding 3D reconstructions in models/.

Further Information

GAN training

This repository uses Lars Mescheder's awesome framework for GAN training.

NeRF

We base our code for the Generator on this great Pytorch reimplementation of Neural Radiance Fields.

Code for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss"

PurNet Project for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss" Abstract Image-based salie

Jinming Su 4 Aug 25, 2022
make ASCII Art by Deep Learning

DeepAA This is convolutional neural networks generating ASCII art. This repository is under construction. This work is accepted by NIPS 2017 Workshop,

OsciiArt 1.4k Dec 28, 2022
Gans-in-action - Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks

GANs in Action by Jakub Langr and Vladimir Bok List of available code: Chapter 2: Colab, Notebook Chapter 3: Notebook Chapter 4: Notebook Chapter 6: C

GANs in Action 914 Dec 21, 2022
Example how to deploy deep learning model with aiohttp.

aiohttp-demos Demos for aiohttp project. Contents Imagetagger Deep Learning Image Classifier URL shortener Toxic Comments Classifier Moderator Slack B

aio-libs 661 Jan 04, 2023
A Structured Self-attentive Sentence Embedding

Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR

Kaushal Shetty 488 Nov 28, 2022
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks Abstract Facial expression recognition in video

Bogireddy Sai Prasanna Teja Reddy 103 Dec 29, 2022
Implementation of BI-RADS-BERT & The Advantages of Section Tokenization.

BI-RADS BERT Implementation of BI-RADS-BERT & The Advantages of Section Tokenization. This implementation could be used on other radiology in house co

1 May 17, 2022
Code for Active Learning at The ImageNet Scale.

Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training with torch's DDP.

Zeyad Emam 47 Dec 12, 2022
Pytorch implementation of MalConv

MalConv-Pytorch A Pytorch implementation of MalConv Desciprtion This is the implementation of MalConv proposed in Malware Detection by Eating a Whole

Alexander H. Liu 58 Oct 26, 2022
X-modaler is a versatile and high-performance codebase for cross-modal analytics.

X-modaler X-modaler is a versatile and high-performance codebase for cross-modal analytics. This codebase unifies comprehensive high-quality modules i

910 Dec 28, 2022
Bottleneck Transformers for Visual Recognition

Bottleneck Transformers for Visual Recognition Experiments Model Params (M) Acc (%) ResNet50 baseline (ref) 23.5M 93.62 BoTNet-50 18.8M 95.11% BoTNet-

Myeongjun Kim 236 Jan 03, 2023
Simple ray intersection library similar to coldet - succedeed by libacc

Ray Intersection This project offers a header only acceleration structure library including implementations for a BVH- and KD-Tree. Applications may i

Nils Moehrle 29 Jun 23, 2022
An implementation of RetinaNet in PyTorch.

RetinaNet An implementation of RetinaNet in PyTorch. Installation Training COCO 2017 Pascal VOC Custom Dataset Evaluation Todo Credits Installation In

Conner Vercellino 297 Jan 04, 2023
[ICCV 2021] Learning A Single Network for Scale-Arbitrary Super-Resolution

ArbSR Pytorch implementation of "Learning A Single Network for Scale-Arbitrary Super-Resolution", ICCV 2021 [Project] [arXiv] Highlights A plug-in mod

Longguang Wang 229 Dec 30, 2022
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.

Nonuniform-to-Uniform Quantization This repository contains the training code of N2UQ introduced in our CVPR 2022 paper: "Nonuniform-to-Uniform Quanti

Zechun Liu 60 Dec 28, 2022
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness This repository contains the code used for the exper

H.R. Oosterhuis 28 Nov 29, 2022
Supervised domain-agnostic prediction framework for probabilistic modelling

A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data

The Alan Turing Institute 112 Oct 23, 2022
An All-MLP solution for Vision, from Google AI

MLP Mixer - Pytorch An All-MLP solution for Vision, from Google AI, in Pytorch. No convolutions nor attention needed! Yannic Kilcher video Install $ p

Phil Wang 784 Jan 06, 2023
data/code repository of "C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer"

C2F-FWN data/code repository of "C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer" (https://arxiv.org/abs/

EKILI 46 Dec 14, 2022
This is a clean and robust Pytorch implementation of DQN and Double DQN.

DQN/DDQN-Pytorch This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve: All the experiments are trained

XinJingHao 15 Dec 27, 2022