Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss

Liping Yin; Albert Chua

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arXiv · 2211.11137

Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss

Liping Yin·Albert Chua

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Abstract

In the past decade, exemplar-based texture synthesis algorithms have seen strong gains in performance by matching statistics of deep convolutional neural networks. However, these algorithms require regularization terms or user-added spatial tags to capture long range constraints in images. Having access to a user-added spatial tag for all situations is not always feasible, and regularization terms can be difficult to tune. Thus, we propose a new set of statistics for texture synthesis based on Sliced Wasserstein Loss, create a multi-scale method to synthesize textures without a user-added spatial tag, study the ability of our proposed method to capture long range constraints, and compare our results to other optimization-based, single texture synthesis algorithms.

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BibTeX RIS

Liping Yin, Albert Chua. 2022-11-21. Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss. https://arxiv.org/abs/2211.11137

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