Fully customizable video frame rate up-conversion and video resolution upscaling with sharpening and denoising editor with GUI.
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Updated
Dec 3, 2023 - Python
Frame interpolation is used to increase the frame rate of a video, or to create a slow-motion video without lowering the frame rate.
Fully customizable video frame rate up-conversion and video resolution upscaling with sharpening and denoising editor with GUI.
A fork of the SuperSloMo repository, modified in various ways to experiment with the capabilitiets of the nural net.
Estimating frame[t] given frames frame[t-1] and frame[t+1]
Smooths video by interpolating frames using Many-to-many Splatting for Efficient Video Frame Interpolation
A program designed to upscale your video's FPS
Implementation of the paper "Video Frame Interpolation by Plug-and-Play Deep Locally Temporal Embedding"
Enhance the quality of your videos
Video frame interpolation using the Vimeo-90k dataset.
In this repository, we deal with the task of video frame interpolation with estimated optical flow. To estimate the optical flow we use pre-trained FlowNet2 deep learning model and experiment by fine-tuning it. We explore the interpolation performance on Spheres dataset and Corridor dataset.
HWFI: Hybrid Warping Fusion for Video Frame Interpolation. IJCV 2022
A Novel Approach leveraging Auto-Encoders, LSTM Networks and Maximum Entropy Principle for Video Super-Resolution (Upscaling and Frame Interpolation)
Predictive Frame Interpolation (PIF) Model for video frame prediction and generation. An AI model that could infer a new frame between two existing sequential frames of a video We trained a GAN using youtube footage to predict it’s own existing frames
PyTorch Implementation of "Robust Temporal Super-Resolution for Dynamic Motion Video", ICCVW, AIM2019
Dynamic Frame Interpolation in Wavelet Domain (TIP 2023)
Video frame interpolation using RIFE
Frame Interpolation Refined with Stable Diffusion via Control Net
In this repository, we deal with the task of video frame interpolation with estimated optical flow. To estimate the optical flow we use Lucas-Kanade algorithm, Multiscale Lucas-Kanade algorithm (with iterative tuning), and Discrete Horn-Schunk algorithm. We explore the interpolation performance on Spheres dataset and Corridor dataset.
A clumsy video auto duplication remove and frame interpolate script (mainly for 24fps cfr animation with dup-frames)