Instant Photorealistic Style Transfer: A Lightweight and Adaptive Approach

• URL: http://arxiv.org/abs/2309.10011v2
• Date: Fri, 20 Oct 2023 18:46:28 GMT
• Title: Instant Photorealistic Style Transfer: A Lightweight and Adaptive Approach
• Authors: Rong Liu, Enyu Zhao, Zhiyuan Liu, Andrew Feng, Scott John Easley
• Abstract summary: We propose an Instant Photo Style Transfer (IPST) approach to achieve instant photorealistic style transfer on super-resolution inputs. Our method utilizes a lightweight StyleNet to enable style transfer from a style image to a content image while preserving non-color information. IPST is well-suited for multi-frame style transfer tasks, as it retains temporal and multi-view consistency of the multi-frame inputs.
• Score: 19.80952739530828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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