RIFE is an open-source deep learning framework for video frame interpolation based on Real-Time Intermediate Flow Estimation. It generates intermediate frames between existing video frames to create smoother motion and increase playback frame rates. Its neural network predicts optical flow information and uses it to synthesize new images at selected timestamps. The software supports arbitrary-timestep interpolation, enabling flexible frame generation between two input images. Optimized implementations can perform 720p frame interpolation at more than 30 frames per second on an NVIDIA RTX 2080 Ti. Users can process videos through Python scripts, pretrained models, and Google Colab demonstrations. The project supports research and practical applications involving slow-motion video, animation enhancement, and frame-rate conversion.
Features
- AI-Based Video Frame Interpolation
- Real-Time Intermediate Optical Flow Estimation
- Arbitrary-Timestep Frame Generation
- Video Frame Rate Enhancement
- Pretrained Neural Network Models
- Python and Google Colab Inference Workflows