This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

Overview

GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields

Project Page | Paper | Supplementary | Video | Slides | Blog | Talk

Add Clevr Tranlation Horizontal Cars Interpolate Shape Faces

If you find our code or paper useful, please cite as

@inproceedings{GIRAFFE,
    title = {GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields},
    author = {Niemeyer, Michael and Geiger, Andreas},
    booktitle = {Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
    year = {2021}
}

TL; DR - Quick Start

Rotating Cars Tranlation Horizontal Cars Tranlation Horizontal Cars

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 giraffe using

conda env create -f environment.yml
conda activate giraffe

You can now test our code on the provided pre-trained models. For example, simply run

python render.py configs/256res/cars_256_pretrained.yaml

This script should create a model output folder out/cars256_pretrained. The animations are then saved to the respective subfolders in out/cars256_pretrained/rendering.

Usage

Datasets

To train a model from scratch or to use our ground truth activations for evaluation, you have to download the respective dataset.

For this, please run

bash scripts/download_dataset.sh

and following the instructions. This script should download and unpack the data automatically into the data/ folder.

Controllable Image Synthesis

To render images of a trained model, run

python render.py CONFIG.yaml

where you replace CONFIG.yaml with the correct config file. The easiest way is to use a pre-trained model. You can do this by using one of the config files which are indicated with *_pretrained.yaml.

For example, for our model trained on Cars at 256x256 pixels, run

python render.py configs/256res/cars_256_pretrained.yaml

or for celebA-HQ at 256x256 pixels, run

python render.py configs/256res/celebahq_256_pretrained.yaml

Our script will automatically download the model checkpoints and render images. You can find the outputs in the out/*_pretrained folders.

Please note that the config files *_pretrained.yaml are only for evaluation or rendering, not for training new models: when these configs are used for training, the model will be trained from scratch, but during inference our code will still use the pre-trained model.

FID Evaluation

For evaluation of the models, we provide the script eval.py. You can run it using

python eval.py CONFIG.yaml

The script generates 20000 images and calculates the FID score.

Note: For some experiments, the numbers in the paper might slightly differ because we used the evaluation protocol from GRAF to fairly compare against the methods reported in GRAF.

Training

Finally, to train a new network from scratch, run

python train.py CONFIG.yaml

where you replace CONFIG.yaml with the name of the configuration file you want to use.

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

cd OUTPUT_DIR
tensorboard --logdir ./logs

where you replace OUTPUT_DIR with the respective output directory. For available training options, please take a look at configs/default.yaml.

2D-GAN Baseline

For convinience, we have implemented a 2D-GAN baseline which closely follows this GAN_stability repo. For example, you can train a 2D-GAN on CompCars at 64x64 pixels similar to our GIRAFFE method by running

python train.py configs/64res/cars_64_2dgan.yaml

Using Your Own Dataset

If you want to train a model on a new dataset, you first need to generate ground truth activations for the intermediate or final FID calculations. For this, you can use the script in scripts/calc_fid/precalc_fid.py. For example, if you want to generate an FID file for the comprehensive cars dataset at 64x64 pixels, you need to run

python scripts/precalc_fid.py  "data/comprehensive_cars/images/*.jpg" --regex True --gpu 0 --out-file "data/comprehensive_cars/fid_files/comprehensiveCars_64.npz" --img-size 64

or for LSUN churches, you need to run

python scripts/precalc_fid.py path/to/LSUN --class-name scene_categories/church_outdoor_train_lmdb --lsun True --gpu 0 --out-file data/church/fid_files/church_64.npz --img-size 64

Note: We apply the same transformations to the ground truth images for this FID calculation as we do during training. If you want to use your own dataset, you need to adjust the image transformations in the script accordingly. Further, you might need to adjust the object-level and camera transformations to your dataset.

Evaluating Generated Images

We provide the script eval_files.py for evaluating the FID score of your own generated images. For example, if you would like to evaluate your images on CompCars at 64x64 pixels, save them to an npy file and run

python eval_files.py --input-file "path/to/your/images.npy" --gt-file "data/comprehensive_cars/fid_files/comprehensiveCars_64.npz"

Futher Information

More Work on Implicit Representations

If you like the GIRAFFE project, please check out related works on neural representions from our group:

PyTorch implementation of Deformable Convolution

PyTorch implementation of Deformable Convolution !!!Warning: There is some issues in this implementation and this repo is not maintained any more, ple

Wei Ouyang 893 Dec 18, 2022
This repository collects 100 papers related to negative sampling methods.

Negative-Sampling-Paper This repository collects 100 papers related to negative sampling methods, covering multiple research fields such as Recommenda

RUCAIBox 119 Dec 29, 2022
Source Code and data for my paper titled Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question Matching

Description The source code and data for my paper titled Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chin

Zhengxiang Wang 3 Jun 28, 2022
NuPIC Studio is an all­-in-­one tool that allows users create a HTM neural network from scratch

NuPIC Studio is an all­-in-­one tool that allows users create a HTM neural network from scratch, train it, collect statistics, and share it among the members of the community. It is not just a visual

HTM Community 93 Sep 30, 2022
Pretraining on Dynamic Graph Neural Networks

Pretraining on Dynamic Graph Neural Networks Our article is PT-DGNN and the code is modified based on GPT-GNN Requirements python 3.6 Ubuntu 18.04.5 L

7 Dec 17, 2022
😮The official implementation of "CoNeRF: Controllable Neural Radiance Fields" 😮

CoNeRF: Controllable Neural Radiance Fields This is the official implementation for "CoNeRF: Controllable Neural Radiance Fields" Project Page Paper V

Kacper Kania 61 Dec 24, 2022
A colab notebook for training Stylegan2-ada on colab, transfer learning onto your own dataset.

Stylegan2-Ada-Google-Colab-Starter-Notebook A no thrills colab notebook for training Stylegan2-ada on colab. transfer learning onto your own dataset h

Harnick Khera 66 Dec 16, 2022
Code for our SIGCOMM'21 paper "Network Planning with Deep Reinforcement Learning".

0. Introduction This repository contains the source code for our SIGCOMM'21 paper "Network Planning with Deep Reinforcement Learning". Notes The netwo

NetX Group 68 Nov 24, 2022
Learning to Reach Goals via Iterated Supervised Learning

Vanilla GCSL This repository contains a vanilla implementation of "Learning to Reach Goals via Iterated Supervised Learning" proposed by Dibya Gosh et

Christoph Heindl 4 Aug 10, 2022
Learn the Deep Learning for Computer Vision in three steps: theory from base to SotA, code in PyTorch, and space-repetition with Anki

DeepCourse: Deep Learning for Computer Vision arthurdouillard.com/deepcourse/ This is a course I'm giving to the French engineering school EPITA each

Arthur Douillard 113 Nov 29, 2022
This was initially the repo for the project of [email protected] of Asaf Mazar, Millad Kassaie and Georgios Chochlakis named "Powered by the Will? Exploring Lay Theories of Behavior Change through Social Media"

Subreddit Analysis This repo includes tools for Subreddit analysis, originally developed for our class project of PSYC 626 in USC, titled "Powered by

Georgios Chochlakis 1 Dec 17, 2021
Finite difference solution of 2D Poisson equation. Can handle Dirichlet, Neumann and mixed boundary conditions.

Poisson-solver-2D Finite difference solution of 2D Poisson equation Current version can handle Dirichlet, Neumann, and mixed (combination of Dirichlet

Mohammad Asif Zaman 34 Dec 23, 2022
Here we present the implementation in TensorFlow of our work about liver lesion segmentation accepted in the Machine Learning 4 Health Workshop

Detection-aided liver lesion segmentation Here we present the implementation in TensorFlow of our work about liver lesion segmentation accepted in the

Image Processing Group - BarcelonaTECH - UPC 96 Oct 26, 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
Multiple Object Tracking with Yolov5!

Tracking with yolov5 This implementation is for who need to tracking multi-object only with detector. You can easily track mult-object with your well

9 Nov 08, 2022
[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

TransFuser This repository contains the code for the CVPR 2021 paper Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. If you find our

695 Jan 05, 2023
This repository contains the code for the paper "Hierarchical Motion Understanding via Motion Programs"

Hierarchical Motion Understanding via Motion Programs (CVPR 2021) This repository contains the official implementation of: Hierarchical Motion Underst

Sumith Kulal 40 Dec 05, 2022
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (CIKM'17)

RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation This is the implementation of RATE: Overcoming Noise and Spar

Yu Zhang 5 Feb 10, 2022
Multi-Person Extreme Motion Prediction

Multi-Person Extreme Motion Prediction Implementation for paper Wen Guo, Xiaoyu Bie, Xavier Alameda-Pineda, Francesc Moreno-Noguer, Multi-Person Extre

GUO-W 38 Nov 15, 2022
A fast poisson image editing implementation that can utilize multi-core CPU or GPU to handle a high-resolution image input.

Poisson Image Editing - A Parallel Implementation Jiayi Weng (jiayiwen), Zixu Chen (zixuc) Poisson Image Editing is a technique that can fuse two imag

Jiayi Weng 110 Dec 27, 2022