Mask-Guided Discovery of Semantic Manifolds in Generative Models (2105.07273v1)

Abstract: Advances in the realm of Generative Adversarial Networks (GANs) have led to architectures capable of producing amazingly realistic images such as StyleGAN2, which, when trained on the FFHQ dataset, generates images of human faces from random vectors in a lower-dimensional latent space. Unfortunately, this space is entangled - translating a latent vector along its axes does not correspond to a meaningful transformation in the output space (e.g., smiling mouth, squinting eyes). The model behaves as a black box, providing neither control over its output nor insight into the structures it has learned from the data. We present a method to explore the manifolds of changes of spatially localized regions of the face. Our method discovers smoothly varying sequences of latent vectors along these manifolds suitable for creating animations. Unlike existing disentanglement methods that either require labelled data or explicitly alter internal model parameters, our method is an optimization-based approach guided by a custom loss function and manually defined region of change. Our code is open-sourced, which can be found, along with supplementary results, on our project page: https://github.com/bmolab/masked-gan-manifold

Mask-Guided Discovery of Semantic Manifolds in Generative Models: A Technical Analysis

The paper "Mask-Guided Discovery of Semantic Manifolds in Generative Models" proposes a novel approach to enhance the control over and interpretation of generative adversarial networks (GANs), particularly StyleGAN2 trained on the FFHQ dataset. The paper underscores the persistent challenge within GANs related to the entanglement of the latent space, where slight modifications in the latent vectors do not correspond to predictable or meaningful changes in the generated images. This paper introduces a method that identifies smooth paths within this latent space, allowing for localized and semantically interpretable transformations—such as changing facial expressions in a specific region, like the mouth—without affecting other areas.

Methodology

The researchers developed an optimization-based technique that does not rely on labeled data nor modifies the model's internal parameters. This method targets spatially localized regions using a manually defined mask over the generated image. By introducing a loss function $\mathcal{L}_X$ combined with a distance measure—either pixel-wise $L^2$ or LPIPS for perceptual difference—the proposed technique isolates the transformation to the specified region. Crucially, this function is minimized when alterations occur predominantly within the masked area, controlled by a parameter $c$, while changes outside remain minimal.

The process involves optimizing a sequence of latent vectors—using an L-BFGS algorithm—configured along a path determined by a mass-spring physical model. This approach enforces proximity and smooth transitions between adjacent vectors through "spring" and "stiffener" constraints, mitigating abrupt changes and ensuring curvature smoothness. As a result, the latent space path enables the generation of coherent animations highlighting the manifold's traversal.

Results

Experimental results showcase the ability of the proposed method to maintain localized transformations precisely within the defined mask. The paper highlights the adaptability of this approach across varied facial regions and initial generative seeds, asserting the model's flexibility. The systematic application of the loss function coupled with spring constraints produces visually seamless animations of facial transformations—compelling evidence for the method's efficacy.

Implications

The proposed framework presents both practical and theoretical implications. Practically, it offers significant advancements in the controllability of GAN outputs, particularly beneficial for applications requiring fine manipulations, such as virtual character animations or synthetic data generation for model training. Theoretically, this work contributes to the understanding of latent space properties, potentially guiding future explorations into disentangled representations and semantic controls.

Furthermore, the paper addresses ethical concerns surrounding GANs, such as the potential impact on societal norms and the propagation of deepfakes, emphasizing the tool's dual-use nature. These concerns underscore the need for responsible deployment of generative technologies.

Future Prospects

Looking forward, several extensions can be explored from this research. One could adapt this mask-guided manifold exploration to other generative models and datasets, expanding its applicability beyond facial imagery. Additionally, refining this technique to accommodate non-linear and higher-dimensional changes, integrating more dynamic region definitions than simple rectangular masks, represents a promising area for further research.

In conclusion, this paper presents a significant step towards interpreting and navigating the complex latent spaces of GANs, providing a robust framework for generating controlled, semantically meaningful images. The introduced methodology promotes a deeper comprehension of generative models' inner workings and opens avenues for enhanced user control in creative AI applications.