Howard, Reborn.

TL;DR: This week, I wrote the logic for processing real user-given input videos and completely rewrote the first half of the network, and have gotten some amazing results!

Hi, I’m Kai. If you’re new here, let me catch you up to speed:

  1. I’m building a neural network which removes the background from videos of people.

  2. It’s based off of this paper by Soumyadip Sengupta and his team at the University of Washington.

  3. The neural network is named Howard, but if you really like, you can call him Howie.

The way this network functions is simple – it takes two videos as input: one of a person in some environment, and one of just the environment. As long as the camera motion is similar in both shots, the network will output an image sequence where the person has been “cut out” from the environment. If you’ve ever used Zoom’s virtual background feature, it’s like that – but better.

Seasoned Experts vs. One Inexperienced Boi

A week ago, I had just barely gotten a janky, low-quality version of Howard to fully work for the first time. I wrote about it here.

The reason Howard wasn’t working very well is because I have pride issues – by which I mean instead of just implementing the proven algorithm from the academic paper written by folks way smarter than me, I thought to myself “ooh, what if I just tried to come up with my own original architecture? What could go wrong?”

Turns out that in a battle of wits between a teenager with no experience whatsoever and seasoned field experts, the experts tend to win.

This week, I finally just decided to bin my old, terrible architecture and exactly replicate the architecture in Sengupta et. al.‘s paper. And guess what! It works spectacularly!

Here’s how my old architecture faired on some test videos:

Aaand here’s how the new, expert-designed architecture does on those same test videos:

(Author’s Note: It’s very late at night as I write this, and I’m trying to publish it before 1AM to meet my Pioneer Tournament deadline, so if you don’t see the second video yet, don’t worry, it’ll be up shortly – I’m still training the new architecture at the moment but it’s almost ready and it’s super cool!)

Spot the difference, I dare you.

Making Howard Almost Useable

I’d like to talk about how those videos were actually made, because that was the other thing I did this week.

Up until this week, Howard had only ever tried to operate on fake, synthesized training data. Training data is special since it contatins not just an example of an input Howard might see, but also an example of what the correct output for that input is. This means that Howard can learn from his mistakes while operating on the training data.

In a real use case, though, Howard won’t have the right answer – he’ll just have two video files given to him by a user, and he’ll have to make his best guess at what the right way to cut the subject out is.

So, you say, just feed those videos to him and see what he spits out! Well, that doesn’t quite work for one big reason:

The two user-generated videos very likely have significantly different camera translation, rotation and clip length.

This is a problem because it means that the “naive solution” of just taking frame X from the subject video and frame X from the background video and having Howard crunch them doesn’t work at all.

In machine learning, there’s a concept called the train/test domain gap. The idea is that for many problems worth solving with ML, the data you’d feed to the neural network in a real-world usecase and the data you have available to train that network on come from different statistical distributions. In my case, that turns out to correspond to the fact that there doesn’t really exist a large, accessible dataset of moving camera videos of people standing in their natural environment, videos of just that environment, and videos of the people cut out from their environment. I’d be really happy if that kind of dataset did exist, but it doesn’t, so I have to settle for the next-best thing: synthesized training data.

The data that I actually train Howard on is similar to the ideal data, but still part of a very different distribution – for example, the background behind the person and the background that is fed to the network are only ever so slightly misaligned, whereas in real use-cases, those two would be very likely not aligned hardly at all.

So if I can’t make the training data have the same statistical distribution as the real-world use-case data, then I need a way to do the opposite – make the real-world use-case data have as close of a distribution to the training data as I can. Following the advice of Mr. Sengupta, who kindly provided me with some very useful resources and pointers after I emailed him, I’m accomplishing this by using homographies.

A homography (in the context of this project) is a kind of transformation you can apply to an image. Specifically, it’s a transformation that warps the perspective, translation, rotation, and scale of a given query image such that it aligns with a given test image. This is very handy because it means I can warp the user-given background frames to match more closely with the user-given foreground frames, thus significantly closing the domain gap between the real-world and train data!

To sum it all up, here’s what Howard actually does when I give him a pair of real-world videos I want him to process:

For every source frame from the video of the person in their environment…

Example source frame

…Howard:

  1. Grabs a few background frames that are temporally nearby from the video of just the environment (one example is pictured):

Example background frame

  1. Detects hundreds of unique features in each background frame
  2. Matches those features with features detected in the source frame:

Feature matching being done on a background and source frame

  1. Applies a homography transformation to line up the matched sets of features as close as possible
  2. Picks the background frame from the set that aligns the closest with the source frame after being warped:

Best bg frame after warpage

  1. Feeds the source frame and the selected warped background frame to Howard, who processes them and outputs the result as an image with an alpha channel.

This results in a sequence of RGBA PNG files being output to a directory, which I can then use in any modern compositing software however I like!

Conclusion

This week was great! Even though I made a ton of progress, I still have a long ways to go before Howard is ready to be shared fully with the world. That said, I’m totally excited for the next couple of weeks of work, because I do feel like I’m nearing the finish line.

Thanks for reading,

Kai

P.S. – The most fun part of all of this actually turns out frequently to be what sort of craziness Howard outputs when I accidentally break him! Here are some of my favorite psychedelic art pieces I inadvertently caused him to make:

Psychedelic art piece 1 Psychedelic art piece 2 Pyschedelic art piece 3

Who knew Howard was such a talented surrealist?

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