https://github.com/hissain/dnn_vfi
This repository contains a machine learning demo for video frame interpolation (VFI) using three different models: UNet, RIFE, and Mamba. The goal is to predict an intermediate frame (2nd frame) given the 1st and 3rd frames as input. The project is under the MIT license.
https://github.com/hissain/dnn_vfi
dnn frame-interpolation machine-learning python vfi
Last synced: 3 months ago
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This repository contains a machine learning demo for video frame interpolation (VFI) using three different models: UNet, RIFE, and Mamba. The goal is to predict an intermediate frame (2nd frame) given the 1st and 3rd frames as input. The project is under the MIT license.
- Host: GitHub
- URL: https://github.com/hissain/dnn_vfi
- Owner: hissain
- License: mit
- Created: 2025-02-17T16:25:02.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-08T05:50:06.000Z (3 months ago)
- Last Synced: 2025-03-08T06:26:01.793Z (3 months ago)
- Topics: dnn, frame-interpolation, machine-learning, python, vfi
- Language: Jupyter Notebook
- Homepage:
- Size: 42.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Video Frame Interpolation (VFI) - Demo Using DNN
This repository contains a machine learning demo for video frame interpolation (VFI) using three different models: UNet, RIFE, and Mamba. The goal is to predict an intermediate frame (2nd frame) given the 1st and 3rd frames as input. The project is under the MIT license.
## 📂 Project Structure
```
.
├── LICENSE # MIT License
├── README.md # Project Documentation
├── __pycache__/ # Compiled Python files
├── input/ # Sample videos
│ ├── enjoy.mp4
│ ├── glob.mp4
│ └── motion.mp4
├── mamba/ # Mamba-based VFI
│ ├── vfi.ipynb
│ └── vfi_model.py
├── requirements.txt # Required dependencies
├── rife/ # RIFE-based VFI
│ └── RIFE.ipynb
└── unet/ # UNet-based VFI
├── Unet2d.ipynb
└── Unet3d.ipynb
```
## 🚀 Models Implemented
__UNet:__ Convolutional neural network (CNN) based architecture for frame interpolation.
__RIFE:__ Real-time Intermediate Flow Estimation model.
__Mamba:__ A sequence modeling architecture applied to frame interpolation.
## 📌 Features
* Sample videos included for testing.
* Frame extraction and dataset preparation.
* Training notebooks for different models.
## 🔧 Installation
Clone the repository and install dependencies:
```
git clone https://github.com/hissain/dnn_vfi.git
cd dnn_vfi
pip install -r requirements.txt
```
## 📊 Usage
Run the corresponding Jupyter notebooks inside the mamba/, rife/, or unet/ directories to train and evaluate the models.
jupyter notebook
## 📜 License
This project is licensed under the MIT License - see the LICENSE file for details.
## 🤝 Contributing
Feel free to submit issues or pull requests to improve the project!
## 📬 Contact
For any inquiries, reach out to [[email protected]] or create an issue in the repository.