Weakly-Supervised Disentanglement Network for Controllable 3D Face Modeling

Centrala begrepp

A weakly-supervised framework for learning a generative 3D face model with disentangled identity and expression factors, without requiring expression labels.

Sammanfattning

The paper introduces a Weakly-Supervised Disentanglement Framework (WSDF) for learning a controllable 3D face model. The key contributions are:

Neutral Bank Module: A module that generates pseudo-neutral scans for each subject using identity labels, which helps prevent degeneration of the identity latent space and facilitates disentanglement.

Label-Free Second-Order Loss: A loss function that regulates the deformation space of the expression latent code, eliminating extraneous information and enhancing disentanglement without the need for expression labels.

Tensor-Based Recoupling: A non-linear tensor-based mechanism to accurately re-couple the disentangled identity and expression latent codes, enabling the generation of subject-specific expressions.

The proposed WSDF framework is evaluated on the FaceScape and CoMA datasets, demonstrating superior performance in reconstruction, disentanglement, and neutralization compared to state-of-the-art methods, even without the use of expression labels. The framework also exhibits improved generalizability when trained on combined datasets.

Statistik

The norm of the difference between the reconstructed face and the neutral face represents the intensity of expression.
The standard deviation of the distances between the generated neutral faces and their mean represents the compactness of the identity latent space.

Citat

"Neutral Bank Module: We introduce a Neutral Bank module, complemented by a dedicated loss function. This module plays a crucial role in preventing the degeneration of identity-consistency, thereby facilitating the disentanglement of the identity factor."
"Label-Free Second-Order Loss: We propose a label-free second-order loss, designed to enhance disentanglement by eliminating nuisance information within the expression space. This is achieved through the regularization of deformation, resulting in more effective disentanglement."

Viktiga insikter från

3D Face Modeling via Weakly-supervised Disentanglement Network joint Identity-consistency Prior

by Guohao Li,Ho... på arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16536.pdf

Djupare frågor

How can the proposed framework be extended to handle pose variations and other facial attributes beyond identity and expression

The proposed framework can be extended to handle pose variations and other facial attributes beyond identity and expression by incorporating additional branches in the encoder network to learn latent spaces for these attributes. For pose variations, a dedicated branch can be added to capture different head orientations and angles. This branch can be trained using weak supervision, such as pose labels or annotations indicating the direction of the face. By including this additional branch in the encoder, the model can disentangle pose variations from other facial attributes, allowing for more comprehensive control over the generated 3D faces.
Similarly, for other facial attributes like age or ethnicity, separate branches can be introduced in the encoder to learn latent representations specific to these attributes. By providing weak supervision in the form of age labels or ethnicity annotations, the model can disentangle these attributes from identity and expression factors. This extension would enable the generation of 3D faces with diverse facial attributes, offering a more versatile and comprehensive framework for facial modeling.

What are the potential limitations of the tensor-based recoupling mechanism, and how could it be further improved to handle more complex facial deformations

The tensor-based recoupling mechanism, while effective in combining disentangled latent spaces for identity and expression, may have limitations when handling more complex facial deformations. One potential limitation is the scalability of the mechanism to capture intricate variations in facial features that go beyond simple linear combinations of identity and expression factors. Complex facial deformations, such as extreme expressions or non-linear changes in facial structure, may not be accurately represented by the tensor-based fusion approach alone.
To improve the mechanism for handling more complex facial deformations, additional non-linear transformations or more sophisticated fusion techniques can be explored. Introducing non-linear activations or more advanced fusion architectures, such as graph neural networks or attention mechanisms, can enhance the model's ability to capture intricate facial variations. By incorporating these advanced techniques, the recoupling mechanism can better accommodate complex deformations and provide more accurate and detailed representations of 3D facial geometry.

Given the success of the Neutral Bank module, how could similar inductive biases be incorporated to disentangle other facial attributes, such as age or ethnicity, in a weakly-supervised manner

Building on the success of the Neutral Bank module for disentangling identity and expression factors in a weakly-supervised manner, similar inductive biases can be incorporated to handle other facial attributes like age or ethnicity. To disentangle age-related features, a similar approach can be adopted where pseudo ground-truth age representations are learned using age labels or age-related annotations. By training the model to generate neutralized faces that preserve age-invariant features, the model can implicitly learn to disentangle age-related variations from other facial attributes.
For disentangling ethnicity or other attributes, the Neutral Bank concept can be extended by creating dedicated modules for each attribute. By aggregating samples with similar ethnicity or attribute characteristics, the model can learn to extract and preserve attribute-specific information in the latent space. This approach leverages the abundance of identity information in existing datasets to disentangle additional facial attributes in a weakly-supervised manner. By incorporating similar inductive biases tailored to specific facial attributes, the framework can be extended to handle a wide range of facial characteristics beyond identity and expression.