Text-conditioned style transfer enables users to communicate their desired
artistic styles through text descriptions, offering a new and expressive means
of achieving stylization. In this work, we evaluate the text-conditioned image
editing and style transfer techniques on their fine-grained understanding of
user prompts for precise "local" style transfer. We find that current methods
fail to accomplish localized style transfers effectively, either failing to
localize style transfer to certain regions in the image, or distorting the
content and structure of the input image. To this end, we carefully design an
end-to-end pipeline that guarantees local style transfer according to users'
intent. Further, we substantiate the effectiveness of our approach through
quantitative and qualitative analysis. The project code is available at:
this https URL

本研究通过文本描述实现了基于文本条件的风格转换，评估了文本条件图像编辑和风格转换技术对精确 “局部” 风格转换的细粒度理解，发现当前方法在有效完成局部风格转换方面存在问题。因此，我们设计了一个端到端的流程，根据用户的意图，确保局部风格转换的有效性，并通过定量和定性分析证明了我们方法的有效性。

最少：基于本地文本的图像风格迁移

LEAST: "Local" text-conditioned image style transfer

In recent years, there has been increasing interest in applying stylization
on 3D scenes from a reference style image, in particular onto neural radiance
fields (NeRF). While performing stylization directly on NeRF guarantees
appearance consistency over arbitrary novel views, it is a challenging problem
to guide the transfer of patterns from the style image onto different parts of
the NeRF scene. In this work, we propose a stylization framework for NeRF based
on local style transfer. In particular, we use a hash-grid encoding to learn
the embedding of the appearance and geometry components, and show that the
mapping defined by the hash table allows us to control the stylization to a
certain extent. Stylization is then achieved by optimizing the appearance
branch while keeping the geometry branch fixed. To support local style
transfer, we propose a new loss function that utilizes a segmentation network
and bipartite matching to establish region correspondences between the style
image and the content images obtained from volume rendering. Our experiments
show that our method yields plausible stylization results with novel view
synthesis while having flexible controllability via manipulating and
customizing the region correspondences.

我们提出了一种基于局部风格转移的 NeRF 风格化框架，利用哈希网格编码来学习外观和几何组件的嵌入，并通过优化外观分支并保持几何分支固定来实现风格化，使用分割网络和二分图匹配来建立样式图像和内容图像之间的区域对应关系，实验证明我们的方法在新视角合成方面得到了合理的风格化结果，并通过操纵和自定义区域对应关系具有灵活的可控性。