This repository will contain 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_data.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:

novel deep learning research works with PaddlePaddle

Research 发布基于飞桨的前沿研究工作,包括CV、NLP、KG、STDM等领域的顶会论文和比赛冠军模型。 目录 计算机视觉(Computer Vision) 自然语言处理(Natrual Language Processing) 知识图谱(Knowledge Graph) 时空数据挖掘(Spa

1.5k Jan 03, 2023
[ICCV 2021] Instance-level Image Retrieval using Reranking Transformers

Instance-level Image Retrieval using Reranking Transformers Fuwen Tan, Jiangbo Yuan, Vicente Ordonez, ICCV 2021. Abstract Instance-level image retriev

UVA Computer Vision 86 Dec 28, 2022
Harvis is designed to automate your C2 Infrastructure.

Harvis Harvis is designed to automate your C2 Infrastructure, currently using Mythic C2. 📌 What is it? Harvis is a python tool to help you create mul

Thiago Mayllart 99 Oct 06, 2022
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents

BROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents. Given the OCR results of the document image, which

Clova AI Research 94 Dec 30, 2022
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration

Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration This is the official repository for the EMNLP 2021 long pa

70 Dec 11, 2022
Python code for ICLR 2022 spotlight paper EViT: Expediting Vision Transformers via Token Reorganizations

Expediting Vision Transformers via Token Reorganizations This repository contain

Youwei Liang 101 Dec 26, 2022
Persian Bert For Long-Range Sequences

ParsBigBird: Persian Bert For Long-Range Sequences The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many ta

Sajjad Ayoubi 63 Dec 14, 2022
An open-source NLP research library, built on PyTorch.

An Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks. Quic

AI2 11.4k Jan 01, 2023
Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification"

PTR Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification" If you use the code, please cite the following paper: @art

THUNLP 118 Dec 30, 2022
Data and evaluation code for the paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER (EMNLP 2021).

Data and evaluation code for the paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. @inproceedings{tedes

Babelscape 40 Dec 11, 2022
Dust model dichotomous performance analysis

Dust-model-dichotomous-performance-analysis Using a collated dataset of 90,000 dust point source observations from 9 drylands studies from around the

1 Dec 17, 2021
Blazing fast language detection using fastText model

Luga A blazing fast language detection using fastText's language models Luga is a Swahili word for language. fastText provides a blazing fast language

Prayson Wilfred Daniel 18 Dec 20, 2022
Examples of using sparse attention, as in "Generating Long Sequences with Sparse Transformers"

Status: Archive (code is provided as-is, no updates expected) Update August 2020: For an example repository that achieves state-of-the-art modeling pe

OpenAI 1.3k Dec 28, 2022
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

LancoPKU 105 Jan 03, 2023
Package for controllable summarization

summarizers summarizers is package for controllable summarization based CTRLsum. currently, we only supports English. It doesn't work in other languag

Hyunwoong Ko 72 Dec 07, 2022
A Fast Sequence Transducer Implementation with PyTorch Bindings

transducer A Fast Sequence Transducer Implementation with PyTorch Bindings. The corresponding publication is Sequence Transduction with Recurrent Neur

Awni Hannun 184 Dec 18, 2022
PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI

data2vec-pytorch PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI (F

Aryan Shekarlaban 105 Jan 04, 2023
The aim of this task is to predict someone's English proficiency based on a text input.

English_proficiency_prediction_NLP The aim of this task is to predict someone's English proficiency based on a text input. Using the The NICT JLE Corp

1 Dec 13, 2021
An implementation of the Pay Attention when Required transformer

Pay Attention when Required (PAR) Transformer-XL An implementation of the Pay Attention when Required transformer from the paper: https://arxiv.org/pd

7 Aug 11, 2022
Repository for the paper: VoiceMe: Personalized voice generation in TTS

🗣 VoiceMe: Personalized voice generation in TTS Abstract Novel text-to-speech systems can generate entirely new voices that were not seen during trai

Pol van Rijn 80 Dec 29, 2022