PortraVec: 基于图像的肖像向量化与文本引导的操控
PortraVec: Image-Based Portrait Vectorization with Text-Guided Manipulation
AI总结：
PortraVec通过文本引导操控模块和图像引导生成模块，实现像素图像到向量草图的转换，提升面部结构一致性、视觉保真度和语义可控性。
摘要：
尽管肖像草图生成是素描合成的特殊任务，但现有方法多为像素基，限制了其可解释性和可编辑性。随着向量生成技术的发展，用向量元素表示草图可能提供更灵活的操控。然而，由于向量图形的重叠性质和粗略细节建模，现有向量化方法难以捕捉面部完整性和细粒度细节，并缺乏语义控制。为了解决这些问题，我们提出了PortraVec，一种将像素基肖像图像转换为向量草图的框架，具有文本控制。具体而言，我们提出了一个两阶段图像引导生成模块，使用注意力感知的偏移采样来捕捉面部结构并纠正细节偏差，并提出了一个基于区域的参数冻结的文本引导操控模块，以实现局部语义编辑同时保持全局一致性。实验表明，PortraVec在结构一致性、视觉保真度和语义可控性方面优于现有最先进方法。
英文摘要：
While portrait sketch generation is a special task in sketch synthesis, most existing methods are pixel-based, limiting their interpretability and editability. With the rise of vector generation techniques, representing sketches using vector elements may provide more flexible manipulation. However, due to the overlapping nature of vector graphics and the coarse detail modeling, existing vectorization methods struggle to capture facial integrity and fine-grained details, and lack semantic control. To address these issues, we propose PortraVec, a framework for converting pixel-based portrait images into vector sketches with text control. Specifically, we propose a two-stage image-guided generation module using Attention-aware Offset Sampling to capture face structure while correcting detail deviations, and a text-guided manipulation module based on Region-based Parameter Freezing to enable local semantic editing while maintaining global consistency. Experiments show that PortraVec achieves superior structural consistency, visual fidelity, and semantic controllability compared to state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
