1,296 research outputs found
Controlling Perceptual Factors in Neural Style Transfer
Neural Style Transfer has shown very exciting results enabling new forms of
image manipulation. Here we extend the existing method to introduce control
over spatial location, colour information and across spatial scale. We
demonstrate how this enhances the method by allowing high-resolution controlled
stylisation and helps to alleviate common failure cases such as applying ground
textures to sky regions. Furthermore, by decomposing style into these
perceptual factors we enable the combination of style information from multiple
sources to generate new, perceptually appealing styles from existing ones. We
also describe how these methods can be used to more efficiently produce large
size, high-quality stylisation. Finally we show how the introduced control
measures can be applied in recent methods for Fast Neural Style Transfer.Comment: Accepted at CVPR201
FastCLIPstyler: Optimisation-free Text-based Image Style Transfer Using Style Representations
In recent years, language-driven artistic style transfer has emerged as a new
type of style transfer technique, eliminating the need for a reference style
image by using natural language descriptions of the style. The first model to
achieve this, called CLIPstyler, has demonstrated impressive stylisation
results. However, its lengthy optimisation procedure at runtime for each query
limits its suitability for many practical applications. In this work, we
present FastCLIPstyler, a generalised text-based image style transfer model
capable of stylising images in a single forward pass for arbitrary text inputs.
Furthermore, we introduce EdgeCLIPstyler, a lightweight model designed for
compatibility with resource-constrained devices. Through quantitative and
qualitative comparisons with state-of-the-art approaches, we demonstrate that
our models achieve superior stylisation quality based on measurable metrics
while offering significantly improved runtime efficiency, particularly on edge
devices.Comment: Accepted at the 2024 IEEE/CVF Winter Conference on Applications of
Computer Vision (WACV 2024
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