Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Arbitrary timestep interpolation #330

Open
lassefschmidt opened this issue Jul 28, 2023 · 1 comment
Open

Arbitrary timestep interpolation #330

lassefschmidt opened this issue Jul 28, 2023 · 1 comment

Comments

@lassefschmidt
Copy link

Hello there !

First of all, thank you for all the work you have put into this!

I am currently trying out some algorithms for arbitrary timestep frame interpolation and came across your repository, which says in the very first paragraph

It supports arbitrary-timestep interpolation between a pair of images.

Since then, I am trying to get this to work. However, all the recent published models (e.g. the HD_v3 stuff, including what is published under Practical-RIFE) do not support this -- it seems this is only implemented for the RIFE model as published in ArXiV.

But even when I use the ArXiV RIFE model, I cannot load the RIFE_m weights you provide under this link due to shape mismatches. This is a bit unfortunate as a lot of computation power went into the pre-training of those weights. Quote of your paper, pg 8:

We use the Vimeo90K-Septuplet [62] dataset to extend RIFE to support arbitrary-timestep frame interpolation [9,24]. This dataset has 91, 701 sequence with a resolution of 448 × 256, each of which contains 7 consecutive frames. [...] We keep other training setting unchanged and denote the model trained on Vimeo90K-Septuplet as RIFEm.

Is there any chance you can tell me how I can load these weights correctly / provide an updated flownet.pkl file ? Or give me some tips on how to run arbitrary-timestep interpolation with your repo in general?

Thank you!

Best,
Lasse

@hzwer
Copy link
Owner

hzwer commented Jul 31, 2023

You can refer to 'benchmark/HD_multi_4X.py' to run that model.

https://github.com/megvii-research/ECCV2022-RIFE#evaluation

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants