Tag-Based Annotation for Avatar Face Creation
An Ngo Daniel Phelps Derrick Lai Thanyared Wong Lucas Mathias Anish Shivamurthy Mustafa Ajmal Minghao Liu James Davis
Currently, digital avatars can be created manually using human images as
reference. Systems such as Bitmoji are excellent producers of detailed avatar
designs, with hundreds of choices for customization. A supervised learning
model could be trained to generate avatars automatically, but the hundreds of
possible options create difficulty in securing non-noisy data to train a model.
As a solution, we train a model to produce avatars from human images using
tag-based annotations. This method provides better annotator agreement, leading
to less noisy data and higher quality model predictions. Our contribution is an
application of tag-based annotation to train a model for avatar face creation.
We design tags for 3 different facial facial features offered by Bitmoji, and
train a model using tag-based annotation to predict the nose.