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3D Generative Model Latent Disentanglement via Local Eigenprojection.

Simone Foti¹, Bongjin Koo^1,2, Danail Stoyanov¹

¹University College London London UK.

Computer Graphics Forum : Journal of the European Association for Computer Graphics

|November 2, 2023

Summary

This study introduces a new method for creating realistic digital humans by improving control over local shape attributes in generative models. The novel approach enhances disentanglement for better attribute control in 3D mesh generation.

Keywords:

disentanglement generative adversarial networks geometric deep learning variational autoencoder

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Area of Science:

Computer Graphics
Artificial Intelligence
Computational Geometry

Background:

Creating realistic digital humans is challenging.
Current generative models lack control over local shape attributes.
Existing methods struggle with attribute disentanglement in 3D mesh generation.

Purpose of the Study:

To introduce a novel loss function for improved control over local shape attributes in digital human creation.
To enhance disentanglement in generative models for 3D head and body meshes.
To enable better decoupling of attribute creation in 3D mesh generation.

Main Methods:

Developed a novel loss function based on spectral geometry.
Applied the loss function to neural-network-based generative models like variational autoencoders (VAEs) and generative adversarial networks (GANs).
Encouraged latent variables to follow local eigenprojections of identity attributes for improved disentanglement.

Main Results:

The proposed Local Eigenprojection Disentangled (LED) models demonstrate superior disentanglement compared to state-of-the-art methods.
Maintained strong generation capabilities while improving attribute control.
Achieved training times comparable to standard VAE and GAN implementations.

Conclusions:

The LED approach effectively addresses the limitation of local shape attribute control in generative models for 3D meshes.
Spectral geometry-based loss functions offer a promising direction for disentangled representation learning.
This work advances the creation of realistic digital humans with enhanced control over geometric details.