This article discusses a novel method for generating 3D human avatars from 2D image collections. The proposed method aims to produce high-quality images and accurate geometry, particularly when modeling loose clothing. The research team introduces a monolithic design that models both the human body and clothing together, along with multiple discriminators to enhance geometric detail. Experimental results show significant improvements over previous methods in terms of appearance and geometry quality. The authors plan to release the code and models for further exploration.
Learn How to Generate 3D Avatars from 2D Image Collections with this Novel AI Technique
Generative models like Generative Adversarial Networks (GANs) can create realistic images of objects and people. But many applications require virtual 3D avatars that can be posed and viewed from different angles. Existing methods for generating 3D avatars often rely on expensive 3D scans of humans for training, limiting their availability and diversity.
Our new method solves this problem by learning to generate 3D human shapes and textures from 2D images. It achieves state-of-the-art image and geometry quality and effectively models loose clothing.
Key Features:
- Monolithic design that models both the human body and clothing
- Multiple discriminators to enhance geometric detail
- Novel generator design for high image quality and flexible handling of clothing
- Articulation module for realistic movement and positioning of body parts
- Empty-space skipping to optimize rendering and improve efficiency
- 2D discriminators guided by normal information for accurate and visually pleasing results
The experimental results show significant improvement over previous methods in terms of geometry and texture quality. We plan to release the code and models for further exploration.
Check out the Paper and Project Page for more details.
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