Image style transfer occupies an important place in both computer graphics
and computer vision. However, most current methods require reference to
stylized images and cannot individually stylize specific objects. To overcome
this limitation, we propose the "Soulstyler" framework, which allows users to
guide the stylization of specific objects in an image through simple textual
descriptions. We introduce a large language model to parse the text and
identify stylization goals and specific styles. Combined with a CLIP-based
semantic visual embedding encoder, the model understands and matches text and
image content. We also introduce a novel localized text-image block matching
loss that ensures that style transfer is performed only on specified target
objects, while non-target regions remain in their original style. Experimental
results demonstrate that our model is able to accurately perform style transfer
on target objects according to textual descriptions without affecting the style
of background regions. Our code will be available at
this https URL

通过简单的文本描述，我们提出了 “Soulstyler” 框架，让用户可以引导对特定物体进行图像风格化处理。我们介绍了一个大型语言模型来解析文本，识别风格化的目标和具体风格，并结合基于 CLIP 的语义视觉嵌入编码器，使模型能够理解并匹配文本和图像内容。同时，我们还引入了一种新颖的局部文本 - 图像块匹配损失函数，确保风格转换仅针对指定的目标对象，而非目标区域保持原始风格。实验结果表明，我们的模型能够根据文本描述准确进行目标对象的风格转换，而不影响背景区域的风格。