Interpreting the Latent Space of GANs for Semantic Face Editing (1907.10786v3)

Abstract: Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code sampled from a random distribution to a photo-realistic image. Previous work assumes the latent space learned by GANs follows a distributed representation but observes the vector arithmetic phenomenon. In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs. In this framework, we conduct a detailed study on how different semantics are encoded in the latent space of GANs for face synthesis. We find that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations. We explore the disentanglement between various semantics and manage to decouple some entangled semantics with subspace projection, leading to more precise control of facial attributes. Besides manipulating gender, age, expression, and the presence of eyeglasses, we can even vary the face pose as well as fix the artifacts accidentally generated by GAN models. The proposed method is further applied to achieve real image manipulation when combined with GAN inversion methods or some encoder-involved models. Extensive results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable facial attribute representation.

Interpreting the Latent Space of GANs for Semantic Face Editing

The paper "Interpreting the Latent Space of GANs for Semantic Face Editing" by Yujun Shen et al. explores an essential aspect of Generative Adversarial Networks (GANs) by focusing on the semantic interpretation within the latent space of these generative models. The authors introduce a framework referred to as InterFaceGAN, which interprets the latent semantics inherently learned by GANs and leverages this understanding to enable semantic face editing.

At the core of GANs is the mapping of a latent code sampled from a random distribution to a realistic image space. The ability of GANs to generate high-fidelity images has been well-documented; however, the underlying mechanism through which GANs encode various semantic attributes within the latent space remains underexplored. Shen et al. shed light on this area by proposing that the latent space contains distinct linear subspaces corresponding to specific facial attributes such as gender, age, and expressions.

Key Contributions

The contributions of this work are multifaceted:

1. InterFaceGAN Framework: The authors present InterFaceGAN, a novel framework that identifies and interprets the semantics within the latent space of pre-trained GAN models. They provide theoretical and empirical evidence demonstrating that well-trained GANs learn a disentangled representation of semantics after certain linear transformations.
2. Semantic Face Editing: By understanding the latent space's organization, the framework allows for precise control of various facial attributes. This includes common attributes like gender and age, as well as more complex ones such as facial poses and correcting artifacts generally produced by GAN models.
3. Real Image Manipulation: The framework extends to real image manipulation by combining GAN inversion methods or encoder-involved models, thereby enabling real-time edits to attributes in existing photographs.

Implementation and Results

The paper meticulously details experiments conducted on state-of-the-art GAN models, specifically PGGAN and StyleGAN. The authors validate their approach by demonstrating the accuracy of identifying semantic boundaries within the latent space, achieving over 95% classification accuracy for various attributes like pose, smile, and gender on a validation set.

Single Attribute Manipulation

The paper shows that moving the latent code along specific directions can precisely control facial attributes. For instance, the gender attribute can be altered by moving the latent code towards a specific direction, effectively making subtle changes in the synthesized images.

Conditional Manipulation

The paper introduces a conditional manipulation approach to address the entanglement between attributes. By projecting the latent code onto a subspace orthogonal to the conditional attribute, the framework achieves independent control over attributes. For example, eyeglasses can be added to a face while maintaining the person's age and gender constant.

Real-World Application and GAN Inversion

InterFaceGAN is tested on real images where the latent code inversion techniques are applied. Although inversion methods like optimization-based and encoder-based approaches exhibit limitations, the results improve significantly with models like StyleGAN. The ability to extend these techniques to real-world applications without retraining the GAN models showcases the robustness and flexibility of the proposed framework.

Comparisons and Further Implications

The research demonstrates that StyleGAN, with its style-based generator approach, possesses a more disentangled latent space compared to traditional models. However, even for models with less disentangled representations (e.g., PGGAN), the proposed conditional manipulation significantly enhances semantic control, offering practical solutions for face-editing applications.

Future Directions

The work sets the stage for several future research directions in AI and facial attribute synthesis:

• Enhanced GAN Inversion Techniques: Developing more robust inversion methods that can better capture the diversity of real-world images.
• Extended Attribute Control: Exploring additional semantic attributes beyond the ones studied, which could be invaluable in various applications ranging from digital avatars to identity preservation in media.
• Real-time Face Editing Applications: Integrating InterFaceGAN into user-facing applications for real-time, user-friendly image and video editings, such as virtual try-ons or live social media filters.

Conclusion

The paper "Interpreting the Latent Space of GANs for Semantic Face Editing" provides an in-depth analysis of GAN latent spaces and presents a concrete methodology for semantic editing of faces. Utilizing the proposed InterFaceGAN framework, the paper advances the understanding of semantic representations within GANs, enabling nuanced control over generated images while offering promising applications in both synthetic and real-world image manipulation. The detailed empirical results and theoretical insights contribute significantly to the field of generative models and facial attribute editing.