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sniklaus
sepconv-slomo
Python2017–2019
sniklaus / sepconv-slomo

sepconv-slomo

an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

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2Distinct people
2Mentions
0Explicit recommenders
1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month2 dated mentions

Observed mentions: 2017-10-15 19:42:30 UTC to 2019-09-15 22:01:00 UTC.

012017-10-01 00:00:00 UTC to 2017-11-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsOct 20172017-11-01 00:00:00 UTC to 2017-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-12-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2018-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-02-01 00:00:00 UTC to 2018-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-03-01 00:00:00 UTC to 2018-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-04-01 00:00:00 UTC to 2018-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-05-01 00:00:00 UTC to 2018-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-06-01 00:00:00 UTC to 2018-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-07-01 00:00:00 UTC to 2018-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-08-01 00:00:00 UTC to 2018-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-09-01 00:00:00 UTC to 2018-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsSep 20182018-10-01 00:00:00 UTC to 2018-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-11-01 00:00:00 UTC to 2018-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-12-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2019-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-02-01 00:00:00 UTC to 2019-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-03-01 00:00:00 UTC to 2019-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-04-01 00:00:00 UTC to 2019-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-05-01 00:00:00 UTC to 2019-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-06-01 00:00:00 UTC to 2019-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-07-01 00:00:00 UTC to 2019-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-08-01 00:00:00 UTC to 2019-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsSep 2019

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Oct 20172017-10-01 00:00:00 UTC2017-11-01 00:00:00 UTC110
Nov 20172017-11-01 00:00:00 UTC2017-12-01 00:00:00 UTC000
Dec 20172017-12-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
Jan 20182018-01-01 00:00:00 UTC2018-02-01 00:00:00 UTC000
Feb 20182018-02-01 00:00:00 UTC2018-03-01 00:00:00 UTC000
Mar 20182018-03-01 00:00:00 UTC2018-04-01 00:00:00 UTC000
Apr 20182018-04-01 00:00:00 UTC2018-05-01 00:00:00 UTC000
May 20182018-05-01 00:00:00 UTC2018-06-01 00:00:00 UTC000
Jun 20182018-06-01 00:00:00 UTC2018-07-01 00:00:00 UTC000
Jul 20182018-07-01 00:00:00 UTC2018-08-01 00:00:00 UTC000
Aug 20182018-08-01 00:00:00 UTC2018-09-01 00:00:00 UTC000
Sep 20182018-09-01 00:00:00 UTC2018-10-01 00:00:00 UTC000
Oct 20182018-10-01 00:00:00 UTC2018-11-01 00:00:00 UTC000
Nov 20182018-11-01 00:00:00 UTC2018-12-01 00:00:00 UTC000
Dec 20182018-12-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
Jan 20192019-01-01 00:00:00 UTC2019-02-01 00:00:00 UTC000
Feb 20192019-02-01 00:00:00 UTC2019-03-01 00:00:00 UTC000
Mar 20192019-03-01 00:00:00 UTC2019-04-01 00:00:00 UTC000
Apr 20192019-04-01 00:00:00 UTC2019-05-01 00:00:00 UTC000
May 20192019-05-01 00:00:00 UTC2019-06-01 00:00:00 UTC000
Jun 20192019-06-01 00:00:00 UTC2019-07-01 00:00:00 UTC000
Jul 20192019-07-01 00:00:00 UTC2019-08-01 00:00:00 UTC000
Aug 20192019-08-01 00:00:00 UTC2019-09-01 00:00:00 UTC000
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC101

What Hacker News says

2 sources
sniklaus·Sep 15, 2019
I unfortunately did not get the approval to release the context-aware frame interpolation, but one of my older works is open source for research purposes: https://github.com/sniklaus/pytorch-sepconv 1. I am not sure about the scale of the individual loss functions anymore, my apologies. I determined the combination of the two losses via a simple grid search and seeing what works best (plus / minus a magnitude did not make that much of a difference). 2. The points are just splatted to an image plane, more advanced point cloud rendering techniques would be better though. There is no video frame interpolation, each individual frame in the output video is a rendering of the point cloud from a different camera perspective. 3. I am not sure about multi-view stereo, COLMAP still seems like the state of the art for that. But neural networks defin
Read the rest
itely outperform classic techniques for single image depth estimation. 4. Common architectures just did not do as well as I was hoping for so I tried about 1500 model architectures. I started with an architecture that intuitively seemed right and then gradually explored / refined alterations of it. It ultimately was a lot of trial and error.
In 3D Ken Burns Effect from a Single Image

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