sniklaus/sepconv-slomo

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1021 stars · 166 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A reference PyTorch implementation of Video Frame Interpolation via Adaptive Separable Convolution, which generates intermediate frames between two input frames or interpolates whole videos. It uses a custom CUDA separable convolution layer built with CuPy and is strictly for academic use.

Use cases

• interpolate a middle frame between two images
• convert a video to slow motion by adding intermediate frames
• increase video frame rate with deep learning
• reproduce results from the ICCV 2017 separable convolution paper
• benchmark frame interpolation on Middlebury optical flow examples

When to choose

• you need a faithful reference implementation of the adaptive separable convolution paper for research
• you want to interpolate frames or videos on a CUDA GPU with PyTorch
• you are doing academic work and can comply with the non-commercial license

When to avoid

• you need commercial use - the license is academic-only
• you want the author's newer, improved model - use sniklaus/revisiting-sepconv instead
• you lack an NVIDIA GPU or cannot install CuPy

Facets

library · maturity maintenance

video-processing deep-learning image-processing computer-vision deep-learning machine-learning python frame-interpolation video-slow-motion pytorch cupy optical-flow academic-research superseded video gpu linux cuda

Member repositories

For agents

markdown · JSON · MCP: product_card(name="sniklaus/sepconv-slomo")