Tag-Based Annotation for Avatar Face Creation
- URL: http://arxiv.org/abs/2308.12642v1
- Date: Thu, 24 Aug 2023 08:35:12 GMT
- Title: Tag-Based Annotation for Avatar Face Creation
- Authors: An Ngo, Daniel Phelps, Derrick Lai, Thanyared Wong, Lucas Mathias,
Anish Shivamurthy, Mustafa Ajmal, Minghao Liu, James Davis
- Abstract summary: We train a model to produce avatars from human images using tag-based annotations.
Our contribution is an application of tag-based annotation to train a model for avatar face creation.
- Score: 2.498487539723264
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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