{"repo":"sniklaus/sepconv-slomo","free":true,"listed":false,"github":"https://github.com/sniklaus/sepconv-slomo","clone":"git clone https://github.com/sniklaus/sepconv-slomo.git","description":"an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch","language":"Python","stars":1021,"topics":["pytorch","python","cuda","deep-learning","cupy"],"license":null,"category":"machine-learning","readme_excerpt":"This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame Interpolation via Adaptive Separable Convolution [1] using PyTorch. Given two frames, it will make use of adaptive convolution [2] in a separable manner to interpolate the intermediate frame. Should you be making use of our work, please cite our paper [1]. For a reimplemntation of our work, see: https://github.com/martkartasev/sepconv And for another adaptation, consider: https://github.com/HyeongminLEE/pytorch-sepconv For softmax splatting, please see: https://github.com/sniklaus/softmax-splatting setup The separable convolution layer is implemented in CUDA using CuPy, which is why CuPy is a required dependency. It can be installed using pip install cupy or alternatively using one of the provided binary packages as outlined in the CuPy repository. If you plan to process videos, then please also make sure to have pip install moviepy installed. usage To run it on your own pair of frames, use the following command. You can either select the l1 or the lf model, please see our paper for more details. In short, the l1 model should be used for quantitative evaluations and the lf model for qualitative comparisons. To run in on a video, use the following command. For a quick benchmark using examples from the Middlebury benchmark for optical flow, run python benchmark.py . You can use it to easily verify that the provided implementation r","default_branch":null,"files":null,"tree":[],"storefront":"/r/sniklaus","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/sniklaus/sepconv-slomo/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}