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Controllable Style Transfer via Test-time Training of Implicit Neural Representation. (arXiv:2210.07762v1 [cs.CV])
Oct. 17, 2022, 1:16 a.m. | Sunwoo Kim, Youngjo Min, Younghun Jung, Seungryong Kim
cs.CV updates on arXiv.org arxiv.org
We propose a controllable style transfer framework based on Implicit Neural
Representation (INR) 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 …
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