- The paper presents methods to control spatial, color, and scale factors, significantly enhancing neural style transfer quality and flexibility.
- Researchers benefit from spatial control via guided Gram Matrices, enabling distinct stylistic treatments for different image regions.
- Integrating these techniques with Fast NST allows efficient, real-time applications while inspiring further explorations in image perceptual representations.
Controlling Perceptual Factors in Neural Style Transfer
This paper examines enhancements in Neural Style Transfer (NST) by providing control over spatial location, color information, and spatial scale. The authors propose methods that introduce intuitive control, improving stylization quality and flexibility, and mitigating common failure cases in NST.
Spatial Control
The authors present methods for spatial control that involve dividing images into distinct regions for stylization using spatial guidance channels. By computing guided Gram Matrices, NST can apply different styles to different image regions, such as applying one style to the sky and another to the ground. This method also facilitates combining multiple styles from different images, generating new stylistic outcomes. The approach uses eroded guidance channels to enhance separation between style regions, ensuring regions with large receptive fields do not distort the desired stylization.
Color Control
Color control is addressed by separating color and luminance information, allowing stylization while preserving original image colors. Two methods are analyzed: luminance-only transfer and color histogram matching. Both methods produce intriguing results but have their tradeoffs. Luminance-only transfer perfectly preserves color but may introduce unnatural changes due to the loss of color-luminance dependencies, while color matching may result in imperfect color distributions but maintains more cohesive color-stroke structures.
Scale Control
To handle different spatial scales, the authors propose combining fine-scale and coarse-scale features from separate style images. By applying NST from one image's fine-scale to another's coarse-scale structures, new composite styles are crafted, allowing large-scale stylization at desired resolutions. This separation is advantageous for rendering high-resolution outputs, addressing NST's limitations with fixed-size receptive fields.
Application to Fast Neural Style Transfer
The proposed methods also adapt to Fast Neural Style Transfer, making them computationally efficient and suitable for real-time applications. For spatial control, the training of feed-forward networks with spatial guidance channels enables the robust association of channels with styles, making spatially-varying stylization automatic and efficient.
Implications and Future Directions
The contributions of this paper have practical implications for creating more dynamic and versatile image stylizations. The ability to separate and recombine stylistic features in different dimensions provides artists and designers significant flexibility. The methods offer pathways for more practically applicable and aesthetically controlled AI-driven artistic processes.
Theoretically, the paper raises questions of how to further factorize perceptual aspects of style in CNNs, encouraging future work to explore more interpretable image representations. This exploration aligns with the broader goal of obtaining machine vision systems that decompose images into perceptual constants, advancing our understanding of AI in visual aesthetics.
In conclusion, the paper introduces significant improvements to NST by developing nuanced control methods. These advancements not only enhance practical utilization but also contribute to theoretical discussions on the interpretability of neural network-based image processing.