mocogan | MoCoGAN : Decomposing Motion and Content for Video Generation | Machine Learning library

by sergeytulyakov Python Version: Current License: No License

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kandi X-RAY | mocogan Summary

mocogan is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, OpenCV applications. mocogan has no bugs, it has no vulnerabilities and it has low support. However mocogan build file is not available. You can download it from GitHub.

This repository contains an implementation and further details of MoCoGAN: Decomposing Motion and Content for Video Generation by Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, Jan Kautz.

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mocogan has a low active ecosystem.

It has 513 star(s) with 112 fork(s). There are 27 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 25 have been closed. On average issues are closed in 26 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of mocogan is current.

Quality

mocogan has 0 bugs and 0 code smells.

Security

mocogan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

mocogan code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

mocogan does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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mocogan releases are not available. You will need to build from source code and install.

mocogan has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

mocogan saves you 287 person hours of effort in developing the same functionality from scratch.

It has 693 lines of code, 60 functions and 7 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed mocogan and discovered the below as its top functions. This is intended to give you an instant insight into mocogan implemented functionality, and help decide if they suit your requirements.

Save a video using ffmpeg
Sample from num_samples
Sample a tensor
Sample z_m
Sample the content of a video
Generate random z_content generator
Get iteration noise
Sample random images
Performs the discriminator
Splits the input into two dimensions

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mocogan Key Features

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mocogan Examples and Code Snippets

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Community Discussions

Trending Discussions on mocogan

What is meant by Average Content Distance in Videos?

QUESTION

What is meant by Average Content Distance in Videos?

Asked 2019-May-30 at 11:42

I'm reading a research paper on generating/synthesizing videos:
MoCoGAN: Decomposing Motion and Content for Video Generation

To evaluate the generated videos, they have used a metric called 'Average Content Distance'. I couldn't find any material on google related to this. Can anyone please explain what Average Content Distance means?

Here is the snippet from the paper

we first computed the average color of the generated shape in each frame. Each frame was then represented by a 3-dimensional vector. The ACD is then given by the average pairwise L2 distance of the per-frame average color vectors.

What I understood from this is as follows:
For each frame, convert rgb to gray (average of color). Then for successive frame, calculate the l2 distance i.e. sum((Frame1(x,y)-Frame2(x,y))^2)/num_pixels over all pixels for 1st and 2nd frame and similarly for successive frames. This gives ACD. Have I understood it correctly?

Also, how does this metric represents quality of a video? How can this be used to compare qualities of different generated videos? You can also point me towards some references.

Thanks!

...

ANSWER

Answered 2019-May-30 at 11:42

From here

First for every frame, we need to compute a vector that represents the content in that frame. This vector is called as content vector. Then compute the distance between the content vectors of consecutive frames and take their average. This gives the average content distance.

In the paper, they consider 2 kinds of videos.

Shapes dataset: This contains very simple videos of shapes moving around. So, when you compute the average color as sum of intensity values (RGB) of each pixel and average it, you get a 3D vector. This vector remains the same independent of spatial location of the shape. This vector changes only when the shape changes.
Human Actions dataset: Since these videos contain humans, they've used OpenFace to get a vector that represents the face (Although this vector may not represent the complete frame). They've used this vector as a representation for each frame. As long as the person remains same in the video, this vector won't change.

In essence, this vector represents the content in a frame. So, find how much this content vector changes from frame to frame. The claim is that this vector shouldn't change much since it is the same shape moving or same person performing some action.

Source https://stackoverflow.com/questions/54955788

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install mocogan

You can download it from GitHub.
You can use mocogan like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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