In this paper, we propose an Instant Photorealistic Style Transfer (IPST)
approach, designed to achieve instant photorealistic style transfer on
super-resolution inputs without the need for pre-training on pair-wise datasets
or imposing extra constraints. Our method utilizes a lightweight StyleNet to
enable style transfer from a style image to a content image while preserving
non-color information. To further enhance the style transfer process, we
introduce an instance-adaptive optimization to prioritize the photorealism of
outputs and accelerate the convergence of the style network, leading to a rapid
training completion within seconds. Moreover, IPST is well-suited for
multi-frame style transfer tasks, as it retains temporal and multi-view
consistency of the multi-frame inputs such as video and Neural Radiance Field
(NeRF). Experimental results demonstrate that IPST requires less GPU memory
usage, offers faster multi-frame transfer speed, and generates photorealistic
outputs, making it a promising solution for various photorealistic transfer
applications.Comment: 8 pages (reference excluded), 6 figures, 4 table