Samples of thumbnails from 2 clusters are shown below:ģ. Observe how Resnet captures color information very effectively (see all the blue shoes in the second cluster) but sometimes fails to encode shape information (see the first cluster). Almost as simple: we can apply the last hidden layer of an off-the-shelf pretrained image classification network (Resnet 18), and evaluate embedding quality by clustering them. We can see that while the first PC distinguishes between interpretable types of avatars, the twelfth is too broad to be meaningful.Ģ. To evaluate the “quality” of the reduction, we visualize thumbnails on the extremes of the principal components (PCs).

The simplest approach: directly apply PCA to the flattened thumbnail images.

There are many ways to reduce the dimensionality, but here’s a few that we can try. For aesthetic analysis, we need to turn this thumbnail into a semantically meaningful numerical representation. The most natural place to look is the 2D avatar thumbnail that often represents users to one another. If we’re interested in aesthetic diversity, we need to start by characterizing avatar aesthetics. As we’ll discover, there is no “standard” Roblox user, and the fantastical aesthetic variety in our users’ avatars directly reflects the diversity of the user base itself. In Sanskrit, avatar ( अवतार) refers to “an incarnation in human form.” In Roblox, few things reflect a user’s identity more directly than their avatar.

Roblox avatars 2021