Generative Image Modeling using Style and Structure Adversarial Networks (1603.05631v2)

Abstract: Current generative frameworks use end-to-end learning and generate images by sampling from uniform noise distribution. However, these approaches ignore the most basic principle of image formation: images are product of: (a) Structure: the underlying 3D model; (b) Style: the texture mapped onto structure. In this paper, we factorize the image generation process and propose Style and Structure Generative Adversarial Network (S^2-GAN). Our S^2-GAN has two components: the Structure-GAN generates a surface normal map; the Style-GAN takes the surface normal map as input and generates the 2D image. Apart from a real vs. generated loss function, we use an additional loss with computed surface normals from generated images. The two GANs are first trained independently, and then merged together via joint learning. We show our S^2-GAN model is interpretable, generates more realistic images and can be used to learn unsupervised RGBD representations.

Generative Image Modeling using Style and Structure Adversarial Networks

The paper "Generative Image Modeling using Style and Structure Adversarial Networks" by Xiaolong Wang and Abhinav Gupta presents an innovative approach to image generation by employing a novel generative adversarial network (GAN) architecture, termed the Style and Structure GAN (${\text{S}^2}$-GAN). This architecture effectively decouples image generation into two distinct components: structure and style. The former deals with the underlying geometry of the scene, while the latter applies textures and illumination to bring the scene to life.

Methodology

The proposed ${\text{S}^2}$-GAN network is split into two primary subsystems:

1. Structure-GAN: Responsible for generating a surface normal map from a latent vector $\hat{z}$. This map represents the 3D structure of the scene, which serves as a scaffold for the 2D image.
2. Style-GAN: Takes the surface normal map and an additional latent vector $\tilde{z}$ to generate the final 2D image. This stage handles the application of textures and styles over the generated structure.

The training of these networks is sequential and involves first training the Structure-GAN and Style-GAN separately using the NYUv2 RGBD dataset before merging them for joint learning. The integration employs adversarial loss and surface normal prediction loss to ensure that the generated images are aligned with the predicted normal maps.

Strong Numerical Results and Claims

The paper claims that the proposed factorized framework leads to several benefits:

• Interpretability: The separation of style and structure allows a more interpretable generative process.
• Realism: The generated images are more realistic, as evidenced by higher scores in classification tasks when evaluated using pre-trained CNNs.
• Stability: Improved training stability compared to traditional GAN models.
• Unsupervised Learning: The approach provides an opportunity to learn RGBD representations without labeled data.

In user studies, the ${\text{S}^2}$-GAN's outputs were preferred 71% of the time over those produced by traditional DCGAN models, highlighting the efficacy of the factorization approach.

Implications and Future Developments

The implications of this research are significant in various domains, including computer graphics, virtual reality, and robotics, where the realistic generation of images from minimal input data is crucial. The interpretability aspect also opens pathways for improved error diagnosis and model refinement, where faults in the generation process can be traced back to either structure or style factors.

Looking ahead, this factorization approach could lead to advancements in conditional image generation tasks, where adjusting the structure or style factors independently could yield tailored results pertinent to specific applications. Moreover, further exploration into unsupervised learning through ${\text{S}^2}$-GAN might unveil new, efficient representations for both 2D and 3D tasks across computer vision fields.

In conclusion, while the ${\text{S}^2}$-GAN architecture is not without its challenges, particularly in balancing the dual GAN training within a coherent framework, its contributions suggest a promising direction for future research in disentangled representation learning and structured image generation.