#RIFE

The project specializes in real-time intermediate flow estimation for enhanced video frame interpolation. It supports high performance with 30+FPS for 720p, suitable for diverse applications, including post-processing for AI-generated videos. Recent versions improve anime scene interpolation based on advanced research. The project received peer review acceptance at ECCV2022. Users benefit from resources for optical flow estimation and video stitching.

Flowframes is a user-friendly Windows interface for video interpolation, featuring support for PyTorch and NCNN frameworks like RIFE and DAIN. Designed for Vulkan-compatible GPUs, this open-source donationware project provides free builds, with additional features available on Patreon. It offers customizable settings for frame de-duplication and scene changes, alongside automated configurations for quick setup. With Auto-Encode and loop interpolation, it caters to both 2D animations and high-resolution video projects.

Discover innovative methods for frame interpolation and video enhancement using RIFE and SAFA frameworks. This project caters to engineers and developers, emphasizing practical video post-processing features. It prioritizes user experience over traditional PSNR metrics, recommending the 4.25 version for diverse applications. SAFA models facilitate efficient space-time video super-resolution, applicable in various digital media tasks and catering to technical users focused on improving video quality.