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arXiv · 2210.07762

Controllable Style Transfer via Test-time Training of Implicit Neural Representation

Sunwoo Kim·Youngjo Min·Younghun Jung·Seungryong Kim
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Abstract

We propose a controllable style transfer framework based on Implicit Neural Representation that pixel-wisely controls the stylized output via test-time training. Unlike traditional image optimization methods that often suffer from unstable convergence and learning-based methods that require intensive training and have limited generalization ability, we present a model optimization framework that optimizes the neural networks during test-time with explicit loss functions for style transfer. After being test-time trained once, thanks to the flexibility of the INR-based model, our framework can precisely control the stylized images in a pixel-wise manner and freely adjust image resolution without further optimization or training. We demonstrate several applications.

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Sunwoo Kim, Youngjo Min, Younghun Jung, Seungryong Kim. 2022-10-17. Controllable Style Transfer via Test-time Training of Implicit Neural Representation. https://arxiv.org/abs/2210.07762

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