用预训练生成模型提升纹理过滤效果，通用性强。
Generative Texture Filtering
- 分两阶段微调生成模型：小样本有监督+大规模无监督强化学习
- 在未标注数据上通过纹理去除与结构保留的奖励函数优化
- 对复杂纹理场景有效，适合图像修复与内容生成任务
我们提出一种生成式纹理过滤方法，表现出出色的性能和泛化能力。核心思路是充分利用预训练生成模型强大的图像先验知识。为此，我们采用两阶段微调策略：首先在极小规模成对图像上进行有监督微调，然后在大规模未标注数据上，通过量化纹理去除与结构保留质量的奖励函数进行强化学习微调。大量实验表明，该方法显著优于以往方法，能有效处理此前具有挑战性的案例。代码已开源：https://github.com/OnlyZZZZ/Generative_Texture_Filtering。
原文摘要 · Abstract (English)
We present a generative method for texture filtering, which exhibits surprisingly good performance and generalizability. Our core idea is to empower texture filtering by taking full advantage of the strong learned image prior of pre-trained generative models. To this end, we propose to fine-tune a pre-trained generative model via a two-stage strategy. Specifically, we first conduct supervised fine-tuning on a very small set of paired images, and then perform reinforcement fine-tuning on a large-scale unlabeled dataset under the guidance of a reward function that quantifies the quality of texture removal and structure preservation. Extensive experiments show that our method clearly outperforms previous methods, and is effective to deal with previously challenging cases. Our code is available at https://github.com/OnlyZZZZ/Generative_Texture_Filtering.
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