Tag-Based Annotation for Avatar Face Creation
This work addresses data quality issues for developers creating avatar generation systems, but it is incremental as it applies an existing annotation method to a specific domain.
The paper tackles the problem of noisy data in training supervised learning models for automatic avatar face creation by using tag-based annotations, resulting in better annotator agreement and higher quality predictions.
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.