Learning Texture Manifolds with the Periodic Spatial GAN (1705.06566v2)

Abstract: This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise distribution by constructing tensors with different types of dimensions. We call this technique Periodic Spatial GAN (PSGAN). The PSGAN has several novel abilities which surpass the current state of the art in texture synthesis. First, we can learn multiple textures from datasets of one or more complex large images. Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smoothly interpolate between samples in the structured noise space and generate novel samples, which lie perceptually between the textures of the original dataset. In addition, we can also accurately learn periodical textures. We make multiple experiments which show that PSGANs can flexibly handle diverse texture and image data sources. Our method is highly scalable and it can generate output images of arbitrary large size.

• The paper introduces the Periodic Spatial GAN that extends traditional GANs by decomposing the noise tensor into local, global, and periodic dimensions.
• It utilizes a fully convolutional network to generate high-resolution textures that capture complex spatial periodicity and variability.
• Empirical tests demonstrate its scalability and texture morphing capabilities, opening new avenues for texture synthesis in graphics applications.

Learning Texture Manifolds with the Periodic Spatial GAN

The paper "Learning Texture Manifolds with the Periodic Spatial GAN" introduces a sophisticated approach to texture synthesis utilizing generative adversarial networks (GANs). Focusing on the nuanced generation of texture images, the authors propose the Periodic Spatial GAN (PSGAN), a model that extends the input noise distribution through the use of high-dimensional tensors, thereby allowing it to handle a diverse range of textures. This method demonstrates several technical advancements over previous GAN-based texture synthesis methods, particularly in its ability to learn and reproduce textures with complex spatial characteristics like periodicity.

Overview of Methodology

The PSGAN framework is structured around a fully convolutional network that maps a spatial tensor $Z$ with distinct parts for local, global, and periodic dimensions into a generated image $X$. This architecture allows it to maintain translation invariance while introducing cyclostationary processes, thus making it suitable for capturing both periodic and non-periodic texture features. Through the decomposition of the noise tensor into distinct dimensions, the PSGAN achieves notable disentanglement of texture features, facilitating better control over generated texture attributes.

Evaluation and Results

The PSGAN model demonstrates significant proficiency in learning a manifold of textures from either single complex images or a diverse dataset of images. It showcases the ability to synthesize textures that embody features not explicitly present in the training data, extending beyond traditional patch-based or descriptor-based approaches. The authors provide empirical evidence of PSGAN’s capabilities through a series of experiments where the model successfully synthesized varied and stylized textures from datasets containing texture images such as the Oxford Describable Textures Dataset and facade images.

A notable highlight of the PSGAN is its scalability with respect to the output image size, sustaining quality across sizes without increased computational demands. This scalability is particularly pertinent for applications requiring high-resolution texture generation. Furthermore, the model exhibits the intriguing capability of texture morphing, achieved through interpolations in the noise space, indicating a rich representation of texture manifolds.

Discussion of Implications

PSGAN presents several practical implications, especially pertinent in fields like computer graphics and art design, where texture generation and manipulation hold critical value. Its ability to synthesize large, coherent textures and morph between them can enhance processes in texture-heavy applications such as gaming and film production.

Theoretically, PSGAN offers a novel approach to embedding periodicity within GAN architectures, which could be extrapolated to other domains where periodic structures are prevalent, such as time-series or audio data synthesis. This adds to the growing body of work focused on enriching GAN structures to capture more complex data relationships.

Future Directions

While PSGAN introduces innovative strides in texture synthesis, it also opens pathways for future research. Areas of potential exploration include the integration of better stability mechanisms within the training process, thereby addressing issues like mode collapse, which are inherent to GAN models. Additionally, expanding the model’s applicability to capture and generate more complex, non-ergodic patterns could further extend its utility across domains.

Moreover, the combination of explicit symmetric process modeling, as suggested by extending periodic functions to include abstract symmetries or applying it to dynamic data forms like audio, presents intriguing frontiers for forthcoming studies.

In conclusion, the PSGAN sets a substantial precedent in the domain of generative modeling for textures, highlighting both technical sophistication and new potential paths for adversarial networks in learning and synthesizing textured patterns. With continued exploration and refinement, the approaches introduced in this work have the capability to catalyze advancements across multiple applied and theoretical areas in AI.