使用生成模型进行直观、交互式的胡须和头发合成
Intuitive, Interactive Beard and Hair Synthesis with Generative Models
摘要：
我们提出了一种交互式的方法来合成图像中面部毛发的逼真变化，从微妙的编辑到现有的毛发，再到在刮胡子的对象的图像中添加复杂和具有挑战性的毛发。为了避免繁琐和计算昂贵的任务的建模，渲染和合成的3D几何形状的目标发型使用传统的图形管道，我们采用了神经网络管道，合成逼真的和详细的面部毛发的图像直接在目标图像在一秒钟内。该合成由来自用户的简单且稀疏的引导笔划控制，该引导笔划定义目标发型的一般结构和颜色属性。我们定性和定量评估我们选择的方法相比，几种替代方法。我们展示了引人注目的交互式编辑结果与原型用户界面，允许新手用户逐步完善生成的图像，以匹配他们想要的发型，并证明我们的方法也允许灵活和高保真的头皮头发合成。
英文摘要：
We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challenging hair in images of clean-shaven subjects. To circumvent the tedious and computationally expensive tasks of modeling, rendering and compositing the 3D geometry of the target hairstyle using the traditional graphics pipeline, we employ a neural network pipeline that synthesizes realistic and detailed images of facial hair directly in the target image in under one second. The synthesis is controlled by simple and sparse guide strokes from the user defining the general structural and color properties of the target hairstyle. We qualitatively and quantitatively evaluate our chosen method compared to several alternative approaches. We show compelling interactive editing results with a prototype user interface that allows novice users to progressively refine the generated image to match their desired hairstyle, and demonstrate that our approach also allows for flexible and high-fidelity scalp hair synthesis.
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