I’ve been working on a multipart project involving a reimplementation of StyleGAN and a research tool to interact with trained StyleGAN models.

Here’re some example images/gifs from the project:

• UI of the tool
• Spatially isolated animations
• Rectangular image generation with attribute modification
• Automatic facial feature detection and modification without needing labels for the training data:
• Unfortunately, somewhat poor-quality male images

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I published a couple of blogs that go into more detail.

In the first blog, I introduce the project and discuss the results, the implementation, and the training data. I also share the code for the tool and my StyleGAN reimplementation:

https://towardsdatascience.com/animating-ganime-with-stylegan-part-1-4cf764578e

The second part is a tutorial that demonstrates how to use the tool to animate images and detect facial features. In it, I supply a compiled version of the (Windows) tool that can be used to follow the tutorial:

https://towardsdatascience.com/animating-ganime-with-stylegan-the-tool-c5a2c31379d?source=friends_link&sk=eec12e2da8c84b9736d32f697da21689

My background before ML was reverse engineering, so building a tool that simplified visualizing and interacting with the internal representation of a model felt like an important step to understand it better. There were some results I did not expect, like how modifying a single feature map can consistently make the same meaningful changes across many different images (like opening/closing a mouth). Also, the ability of some feature maps to act as facial feature detectors without training labels made me interested in applying the same approach to other types of generative models.

Let me know if you have any questions/comments/corrections/criticisms or know about similar prior work. As this has been a solo project, I’m pretty starved for outside perspectives.