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This project provides a suite of tools and deep learning models for optical flow estimation and video frame interpolation. It is designed to analyze pixel-level motion between consecutive frames to determine the direction and magnitude of movement, using this data to synthesize new intermediate images.
The main features of hzwer/eccv2022-rife are: Video Frame Interpolation Tools, Optical Flow Computation, Real-Time Frame Interpolation, Real-Time Motion Interpolators, Video Clip Stitchers, Video Transition Smoothing.
Projects with overlapping indexed features include: baowenbo/dain — DAIN is a video frame synthesis engine and AI video upsampling tool designed to increase video playback smoothness. It… nvidia/isaac-gr00t. justin62628/squirrel-rife — Squirrel-RIFE is a GPU-accelerated video processing tool that uses a neural network to generate intermediate frames… princeton-vl/raft — RAFT is a PyTorch computer vision framework and deep learning system designed for optical flow estimation. It… tntwise/real-video-enhancer — Real-Video-Enhancer is a cross-platform desktop application that utilizes neural networks to upscale resolution,… aaronfeng753/waifu2x-extension-gui — Waifu2x-Extension-GUI is a desktop application designed for high-fidelity media restoration and enhancement. It…
DAIN is a video frame synthesis engine and AI video upsampling tool designed to increase video playback smoothness. It functions as a computer vision model that synthesizes intermediate frames between existing images to transform low frame rate video into high frame rate content. The system utilizes depth-aware video frame interpolation to predict the motion of pixels between consecutive images. By analyzing spatial depth via depth maps, the tool generates new frames that account for occlusions and overlapping objects to create slow motion effects. The framework incorporates optical flow int
Squirrel-RIFE is a GPU-accelerated video processing tool that uses a neural network to generate intermediate frames between existing video frames, enabling smooth slow-motion effects and frame rate conversion. It is built around the RIFE (Real-Time Intermediate Flow Estimation) model, which analyzes motion between consecutive frames to predict and insert new frames, and leverages NVIDIA CUDA for parallel processing to achieve high-speed inference. The tool distinguishes itself by combining neural frame interpolation with practical video preprocessing features, including pixel-level duplicate
RAFT is a PyTorch computer vision framework and deep learning system designed for optical flow estimation. It functions as a GPU-accelerated motion estimator that calculates per-pixel motion vectors between video frames to determine object movement. The implementation utilizes recurrent all-pairs field transforms and custom CUDA kernels to optimize the memory and compute overhead associated with high-dimensional correlation calculations. This hardware-level acceleration reduces GPU memory usage during the forward pass. The toolkit covers supervised flow learning and model training using mixe
