Tag-Based Annotation for Avatar Face Creation

TL;DR

This paper introduces a tag-based annotation approach to train a model for automatic avatar face creation, improving data quality and prediction accuracy by reducing noise in annotations.

Contribution

It applies tag-based annotation to train a model for avatar face generation, specifically focusing on predicting facial features like the nose.

Findings

Enhanced annotation consistency with tag-based approach

Higher quality avatar predictions achieved

Reduced noise in training data

Abstract

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.