3D Avatar GAN Adaptation for Artistic Style and Geometry Fidelity
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Solution Overview
Problem
Existing generative adversarial networks (GANs) struggle to generate 3D avatars with artistic styles due to the arbitrary distribution of geometry and texture in artistic datasets, leading to challenges in maintaining subject identity and achieving high-fidelity geometry and texture.
Innovation Solution
A domain adaptation framework is employed, utilizing a pre-trained 3D GAN and a 2D GAN, where the 2D GAN is trained on artistic datasets to produce a 3D Avatar GAN, incorporating modules for camera alignment, feature regularization, geometric deformation, and editing to preserve identity and style.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pre-trained 3D GAN is adapted to artistic datasets, then artistic style is achieved, but subject identity and geometry fidelity deteriorate
Solution Approach 1:
The system segments the GAN architecture into multiple domains: a source domain GAN pre-trained on realistic 3D data, and a target domain GAN trained on artistic datasets. This segmentation allows each domain to specialize - the source domain preserves geometric fidelity and subject identity, while the target domain imparts artistic style, thereby resolving the contradiction between artistic adaptation and geometry preservation
Solution Approach 2:
The system introduces an intermediary adaptation framework that transfers knowledge from the source domain GAN to the target domain GAN. This intermediary process enables the target domain to acquire artistic styles while maintaining the geometric structure and identity information learned from the source domain, thus preserving geometry fidelity during artistic adaptation
2Adaptability or versatility
If artistic datasets are used for training, then artistic style is achieved, but subject identity preservation deteriorates
Solution Approach 1:
The system separates the learning of subject identity from artistic style by using two distinct domains. The source domain GAN is pre-trained on realistic 3D data to learn accurate subject identity and geometric structure. The target domain GAN is then adapted to artistic datasets to learn style characteristics. This segmentation ensures that subject identity is preserved from the source domain while artistic style is acquired from the target domain
Solution Approach 2:
The source domain GAN is pre-trained on realistic 3D data before adapting to artistic datasets. This preliminary action establishes a strong foundation of subject identity and geometric fidelity that is then transferred to the target domain during adaptation, ensuring that subject identity is preserved even when artistic styles are applied
Data Source
AI summary
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.