藝術風格轉換是一種影象的合成問題，其中影象的內容是以另一種風格再現的。

Artistic style transfer is an image synthesis problem where the contentof an image is reproduced with the style of another.

近年來的研究表明，利用預訓練卷積神經網路的隱式啟用，可以實現具有視覺吸引力的風格轉換。

Recent works show that a visually appealing style transfer can beachieved by using the hidden activations of a pretrained convolutional neuralnetwork.

然而，現有的方法要麼(i)適用於任何樣式的影象但非常複雜的優化過程，要麼(ii)採用有效前饋網路，僅實現有限數量的轉換風格。

However, existing methods either apply (i) an optimization procedurethat works for any style image but is very expensive, or (ii) an efficientfeedforward network that only allows a limited number of trained styles.

在本文中，我們提出了一個更簡單的基於區域性匹配的優化目標，該目標融合了單層預訓練網路的內容結構和樣式紋理。

In this work we propose a simpler optimization objective based on localmatching that combines the content structure and style textures in a singlelayer of the pretrained network.

我們證明了我們的結果具有令人滿意的特性，如更簡單的優化環境、直觀的引數調諧、以及視訊上一致的逐幀展示效能。

We show that our objective has desirable properties such as a simpleroptimization landscape, intuitive parameter tuning, and consistentframe-by-frame performance on video.

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