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.

翻译：目前，数字虚拟头像可通过参考人类图像手动创建。Bitmoji等系统能生成细节丰富的头像设计，提供数百种定制选项。虽然可以训练监督学习模型自动生成头像，但数百种可选方案导致获取无噪声数据训练模型存在困难。为此，我们提出基于标签注释的方法训练模型，从人类图像生成头像。该方法能提升标注者间的一致性，从而降低数据噪声并提高模型预测质量。本文贡献在于将基于标签的注释应用于虚拟头像面部生成模型训练：我们针对Bitmoji提供的三种不同面部特征设计标签体系，并通过基于标签的注释训练模型实现鼻部特征的预测。