用少量样本快速生成高保真人脸素描，保持身份特征不变。
PS-StyleGAN: Illustrative Portrait Sketching using Attention-Based Style Adaptation
- 通过注意力调制的仿射块，动态调整风格潜码以生成素描
- 仅需约100对图像训练，生成结果在多个数据集上超越现有方法
- 适合需要快速定制素描风格的设计与艺术创作场景
人脸素描需捕捉真实面部的身份特异性特征，同时以抽象线条和明暗表现。与照片级图像不同，高质量素描生成要求对细节有选择性关注，因此极具挑战性。本文提出针对人脸素描合成的专用风格迁移方法——PS-StyleGAN，利用StyleGAN的语义W+潜空间生成素描，可在不损失身份一致性的前提下实现姿态与表情的可控编辑。我们设计了基于注意力的仿射变换模块，使模型能同时感知内容与风格特征，动态调整风格潜码，实现逆向一致的风格适配。该方法仅需约100对配对样本即可建模新风格，训练时间短。在多个数据集上，我们的方法在定性和定量评估中均优于当前最优技术。
原文摘要 · Abstract (English)
Portrait sketching involves capturing identity specific attributes of a real face with abstract lines and shades. Unlike photo-realistic images, a good portrait sketch generation method needs selective attention to detail, making the problem challenging. This paper introduces \textbf{Portrait Sketching StyleGAN (PS-StyleGAN)}, a style transfer approach tailored for portrait sketch synthesis. We leverage the semantic $W+$ latent space of StyleGAN to generate portrait sketches, allowing us to make meaningful edits, like pose and expression alterations, without compromising identity. To achieve this, we propose the use of Attentive Affine transform blocks in our architecture, and a training strategy that allows us to change StyleGAN's output without finetuning it. These blocks learn to modify style latent code by paying attention to both content and style latent features, allowing us to adapt the outputs of StyleGAN in an inversion-consistent manner. Our approach uses only a few paired examples ($\sim 100$) to model a style and has a short training time. We demonstrate PS-StyleGAN's superiority over the current state-of-the-art methods on various datasets, qualitatively and quantitatively.
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