StyleLight: HDR Panorama Generation for Lighting Estimation and Editing

Project | YouTube | arXiv

Abstract: We present a new lighting estimation and editing framework to generate high-dynamic-range (HDR) indoor panorama lighting from a single limited field-of-view (FOV) image captured by low-dynamic-range (LDR) cameras. Existing lighting estimation methods either directly regress lighting representation parameters or decompose this problem into FOV-to-panorama and LDR-to-HDR lighting generation sub-tasks. However, due to the partial observation, the high-dynamic-range lighting, and the intrinsic ambiguity of a scene, lighting estimation remains a challenging task. To tackle this problem, we propose a coupled dual-StyleGAN panorama synthesis network (StyleLight) that integrates LDR and HDR panorama synthesis into a unified framework. The LDR and HDR panorama synthesis share a similar generator but have separate discriminators. During inference, given an LDR FOV image, we propose a focal-masked GAN inversion method to find its latent code by the LDR panorama synthesis branch and then synthesize the HDR panorama by the HDR panorama synthesis branch. StyleLight takes FOV-to-panorama and LDR-to-HDR lighting generation into a unified framework and thus greatly improves lighting estimation. Extensive experiments demonstrate that our framework achieves superior performance over state-of-the-art methods on indoor lighting estimation. Notably, StyleLight also enables intuitive lighting editing on indoor HDR panoramas, which is suitable for real-world applications.

Guangcong Wang, Yinuo Yang, Chen Change Loy, Ziwei Liu

S-Lab, Nanyang Technological University

In European Conference on Computer Vision (ECCV), 2022

0. Update

• [2023-04-19] Panorama warping. If you render an object is at the center of the panorama, you can warp the panoramas with warping.py.

1. Prerequisites

• Linux or macOS
• Python 3
• NVIDIA GPU + CUDA cuDNN(10.2)
• PyTorch >= 1.7
• OpenCV

2. Installation

We recommend using the virtual environment (conda) to run the code easily.

conda create -n StyleLight python=3.7 -y
conda activate StyleLight
pip install lpips
pip install wandb
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.2 -c pytorch

pip install matplotlib
pip install dlib
pip install imageio
pip install einops

sudo apt-get install openexr and libopenexr-dev
pip install OpenEXR

pip install imageio-ffmpeg
pip install ninja
pip install opencv-python

3. Training

Download dataset

• Please download the Laval dataset from the official website.

Pre-process datasets

• Set the variables the path to the laval dataset(from_folder) and the path to save pre-processed data (to_folder) in data_prepare_laval.py

python data_prepare_laval.py

Train StyleLight

python train.py --outdir=./training-runs-256x512 --data=/mnt/disks/data/datasets/IndoorHDRDataset2018-128x256-data-splits/train --gpus=8 --cfg=paper256  --mirror=1 --aug=noaug

• --outdir is the path to save models and generated examples
• --gpus is the number of gpus
• --data is the path to the pre-processed data
• --cfg is the configure of stylegan-ada
• --mirror and --aug is data augmentation

Or download inference model

• Please download the inference model from the Google Drive.

4. Test

Lighting estimation and editing

• Set paths (stylegan2_ada_ffhq) in PTI_utils/paths_config.py
• Set options (lighting estimation or lighting editing) in PTI_utils/hyperparameters.py

python test_lighting.py

5. To-Do

• Training code
• Inference model
• Evaluation code
• Clean Code
• Colab Demo

6. Citation

If you find this useful for your research, please cite the our paper.

@inproceedings{wang2022stylelight,
   author    = {Wang, Guangcong and Yang, Yinuo and Loy, Chen Change and Liu, Ziwei},
   title     = {StyleLight: HDR Panorama Generation for Lighting Estimation and Editing},
   booktitle = {European Conference on Computer Vision (ECCV)},   
   year      = {2022},
  }

or

Guangcong Wang, Yinuo Yang, Chen Change Loy, and Ziwei Liu. StyleLight: HDR Panorama Generation for Lighting Estimation and Editing, ECCV 2022.

7. Related Links

Text2Light: Zero-Shot Text-Driven HDR Panorama Generation, TOG 2022 (Proc. SIGGRAPH Asia)

CaG: Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs, Technical report, 2022

SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections, Arxiv 2023

Relighting4D: Neural Relightable Human from Videos, ECCV 2022

Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis, ECCV 2022

Gardner et al. Learning to Predict Indoor Illumination from a Single Image, SIGGRAPH Asia, 2017.

Gardner et al. Deep Parametric Indoor Lighting Estimation, ICCV 2019.

Zhan et al. EMlight:Lighting Estimation via Spherical Distribution Approximation, AAAI 2021.

8. Acknowledgments

This code is based on the StyleGAN2-ada-pytorch, PTI, and skylibs codebases. We also thank Jean-François Lalonde for sharing experience.