论文标题
可控样式转移通过隐式神经表示的测试时间培训
Controllable Style Transfer via Test-time Training of Implicit Neural Representation
论文作者
论文摘要
我们提出了一个基于隐式神经表示形式的可控样式转移框架，该框架通过测试时间培训来控制风格化的输出。与传统的图像优化方法通常受到不稳定的收敛性和基于学习的方法，需要进行密集培训并且具有有限的概括能力，我们提出了一个模型优化框架，该框架在测试时间期间优化了神经网络，并具有明显的样式转移损失功能。经过测试时间训练一次后，由于基于INR的模型的灵活性，我们的框架可以精确地以像素的方式控制风格化的图像，并在没有进一步优化或培训的情况下自由调整图像分辨率。我们演示了几种应用。
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.
