From Sim-to-Real: Toward General Event-based Low-Light Frame Interpolation with Per-scene Optimization

Ziran Zhang^1,2, Yongrui Ma^3,2, Yueting Chen¹, Feng Zhang², Jinwei Gu³, Tianfan Xue³, Shi Guo²

¹ Zhejiang University, ² Shanghai AI Laboratory, ³ The Chinese University of Hong Kong

🌐 Project Page | 🎥 Video | 📄 Paper | 📊 Data | 🛠️ Weights

🔔 Overview

This repository hosts the implementation of "From Sim-to-Real: Toward General Event-based Low-Light Frame Interpolation with Per-scene Optimization" (SIGGRAPH Asia 2024). Our approach leverages event cameras and enhances Video Frame Interpolation (VFI) in low-light conditions via a per-scene optimization strategy. This method adapts the model to specific lighting and camera settings, solving issues like trailing artifacts and signal degradation common in low-light environments.

🔑 Key Features

• Per-Scene Optimization: Fine-tunes a pre-trained model for each scene, significantly improving interpolation results in varied lighting conditions.
• Low-Light Event Correction: Effectively mitigates event-based signal latency and noise under low-light conditions.
• EVFI-LL Dataset: Provides challenging RGB+Event sequences captured in low-light environments for benchmarking.

🚀 Quick Start: Per-Scene Optimization

Follow these steps to apply per-scene optimization with pre-trained models:

1. Clone the repository:

git clone https://github.com/OpenImagingLab/Sim2Real.git
cd Sim2Real/Sim2Real_code

2. Download and place the required weights:

• Main model weights: Place in pretrained_weights/
• DISTS loss function weights: Place in losses/DISTS/weights/

Download Weights Here

3. Install dependencies:

pip install -r requirements.txt

4. Run per-scene optimization:

bash perscene.sh

This will fine-tune the pre-trained model on specific scenes, performing frame interpolation optimized for each setting.

🛠️ Optional: Pretraining the Model

To pretrain the model from scratch using simulated data:

1. Pretrain the model:

bash pretrain.sh

2. After pretraining, proceed with per-scene optimization as described above.

📂 Directory Structure

• dataset/: Utilities for dataset preparation and loading.
• losses/: Custom loss functions and weights for training.
• models/: Neural network models for Sim2Real frame interpolation tasks.
• params/: Configuration files for training and evaluation.
• tools/: Scripts for preprocessing and postprocessing.
• pretrained_weights/: Directory for storing pre-trained models.
• run_network.py: Main script for training and evaluation.
• pretrain.sh: Script for model pretraining.
• perscene.sh: Script for per-scene optimization.
• requirements.txt: Required Python dependencies.

📊 EVFI-LL Dataset

The EVFI-LL dataset includes RGB+Event sequences captured under low-light conditions, offering a challenging benchmark for evaluating event-based VFI performance. Download and place the dataset in the dataset/ directory.

Download Dataset Here

📜 License Information

The code in this repository is licensed under the MIT License.

📝 Citation

If you find this work helpful in your research, please cite:

@article{zhang2024sim,
  title={From Sim-to-Real: Toward General Event-based Low-light Frame Interpolation with Per-scene Optimization},
  author={Zhang, Ziran and Ma, Yongrui and Chen, Yueting and Zhang, Feng and Gu, Jinwei and Xue, Tianfan and Guo, Shi},
  journal={arXiv preprint arXiv:2406.08090},
  year={2024}
}

🙏 Acknowledgements

This project builds upon the exceptional work of TimeLens-XL. We extend our sincere thanks to the original authors for their outstanding contributions.