To extend the proposed compositional neural texture representation to handle more complex, non-stationary textures that lack sufficient self-repetitions, several strategies can be employed:
Augmented Data Generation: By incorporating data augmentation techniques such as rotation, scaling, and flipping, the model can be exposed to a wider variety of textures, including non-stationary ones. This augmented data can help the model learn to represent and manipulate diverse textures effectively.
Adaptive Gaussian Modeling: Instead of relying solely on fixed Gaussian representations, the model can adaptively adjust the shape and number of Gaussians based on the complexity and variability of the texture. This adaptive modeling can help capture the intricate details and variations present in non-stationary textures.
Hierarchical Texton Representation: Introducing a hierarchical structure to the texton representation can enable the model to capture textures at multiple scales and levels of abstraction. This hierarchical approach can better handle the complexity and non-stationarity of textures by representing them in a more structured and organized manner.
Incorporating Spatial Context: By considering the spatial relationships and context between textons, the model can better understand the overall texture composition and arrangement. This spatial context can help in capturing the non-stationary nature of textures and improving the representation of complex patterns and variations.
Advanced Feature Encoding: Enhancing the feature encoding process with more advanced techniques such as attention mechanisms, graph neural networks, or self-supervised learning can enable the model to extract more informative and discriminative features from non-stationary textures. These advanced features can enhance the representation and manipulation of complex textures.