Unknown Organization (20240221281). AVATAR GENERATION ACCORDING TO ARTISTIC STYLES simplified abstract
Contents
AVATAR GENERATION ACCORDING TO ARTISTIC STYLES
Organization Name
Inventor(s)
Rameen Abdal of Los Angeles CA (US)
Menglei Chai of Los Angeles CA (US)
Hsin-Ying Lee of San Jose CA (US)
Aliaksandr Siarohin of Los Angeles CA (US)
Sergey Tulyakov of Santa Monica CA (US)
Peihao Zhu of Los Angeles CA (US)
AVATAR GENERATION ACCORDING TO ARTISTIC STYLES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240221281 titled 'AVATAR GENERATION ACCORDING TO ARTISTIC STYLES
Simplified Explanation: This patent application describes a domain adaptation framework for creating a 3D avatar Generative Adversarial Network (GAN) that can generate an avatar from a single photographic image.
- The framework uses artistic datasets with different style types like caricature, cartoon, and comic to train the target domain.
- It starts with a source domain trained with a 3D GAN and a target domain trained with a 2D GAN.
- The 2D GAN is fine-tuned using the artistic datasets to generate a 3D avatar GAN capable of producing 3D artistic avatars and an editing module for semantic and geometric edits.
Key Features and Innovation:
- Utilizes domain adaptation framework for creating a 3D avatar GAN.
- Trains target domain with artistic datasets of different style types.
- Fine-tunes 2D GAN with artistic datasets to generate 3D avatar GAN.
Potential Applications:
- Virtual reality applications.
- Gaming industry for character creation.
- Animation and film industry for avatar generation.
Problems Solved:
- Generating 3D avatars from 2D images.
- Enhancing artistic avatar creation.
- Improving editing capabilities for avatars.
Benefits:
- Efficient creation of 3D avatars.
- Diverse style options for avatars.
- Enhanced editing features for customization.
Commercial Applications:
- Virtual reality content creation tools.
- Character customization in video games.
- Avatar creation services for social media platforms.
Questions about 3D Avatar GAN: 1. How does the domain adaptation framework improve the generation of 3D avatars? 2. What are the potential limitations of using artistic datasets for training the target domain?
Frequently Updated Research: Stay updated on advancements in GAN technology for avatar generation and domain adaptation techniques for improved 3D modeling.
Original Abstract Submitted
domain adaptation frameworks for producing a 3d avatar generative adversarial network (gan) capable of generating an avatar based on a single photographic image. the 3d avatar gan is produced by training a target domain using an artistic dataset. each artistic dataset includes a plurality of source images, each associated with a style type, such as caricature, cartoon, and comic. the domain adaptation framework in some implementations starts with a source domain that has been trained according to a 3d gan and a target domain trained with a 2d gan. the framework fine-tunes the 2d gan by training it with the artistic datasets. the resulting 3d avatar gan generates a 3d artistic avatar and an editing module for performing semantic and geometric edits.
