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Real-time video frame interpolation using intermediate flow estimation.

RIFE (Real-Time Intermediate Flow Estimation) is an open-source project implementing video frame interpolation using deep learning. It estimates intermediate frames between existing video frames to increase the frame rate or create slow-motion effects. The core architecture involves optical flow estimation and refinement networks, allowing the generation of new frames based on the movement and appearance information extracted from the input frames. It can run at 30+ FPS for 2x 720p interpolation on a 2080Ti GPU. RIFE supports arbitrary-timestep interpolation. The model is implemented in Python using PyTorch, and Dockerfiles are provided for containerization and easy deployment. It also features pre-trained models for HD video and supports GPU acceleration. It has been optimized for anime scenes and diffusion model generated videos. It enables functionalities like video stitching and optical flow estimation.
RIFE (Real-Time Intermediate Flow Estimation) is an open-source project implementing video frame interpolation using deep learning.
Explore all tools that specialize in interpolate video frames. This domain focus ensures Real-Time Intermediate Flow Estimation (RIFE) delivers optimized results for this specific requirement.
Explore all tools that specialize in optical flow estimation. This domain focus ensures Real-Time Intermediate Flow Estimation (RIFE) delivers optimized results for this specific requirement.
Supports interpolation at any point between two frames, allowing for fine-grained control over slow-motion effects.
Provides Dockerfiles for containerization, simplifying deployment and ensuring consistent performance across different environments.
Leverages GPUs for faster processing, enabling real-time performance for high-resolution video interpolation.
Offers pre-trained models for high-definition video, reducing the need for extensive training and fine-tuning.
Estimates the motion between frames to generate intermediate frames, ensuring smooth transitions and realistic effects.
Install git.
Clone the repository: git clone git@github.com:megvii-research/ECCV2022-RIFE.git
Navigate to the directory: cd ECCV2022-RIFE
Install requirements: pip3 install -r requirements.txt
Download pretrained HD models and place in train_log/*
Run video interpolation: python3 inference_video.py --exp=1 --video=video.mp4
All Set
Ready to go
Verified feedback from other users.
"RIFE is highly praised for its real-time performance and high-quality video frame interpolation capabilities, making it a valuable tool for enhancing video content."
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