论文标题

与生成模型的直观,互动胡须和头发合成

Intuitive, Interactive Beard and Hair Synthesis with Generative Models

论文作者

Olszewski, Kyle, Ceylan, Duygu, Xing, Jun, Echevarria, Jose, Chen, Zhili, Chen, Weikai, Li, Hao

论文摘要

我们提出了一种互动方法,可以在图像中综合面部毛发的现实变化,从微妙的编辑到现有的头发,再到清洁剃须受试者图像中的复杂而具有挑战性的头发。为了避免使用传统图形管道的建模,渲染和合成目标发型的3D几何的繁琐和计算昂贵的任务,我们采用了一条神经网络管道,该神经网络管道合成了一秒钟的目标图像中直接在目标图像中直接在目标图像中综合面部毛发的现实图像。该合成由用户的简单而稀疏的指南触发,这些用户定义了目标发型的一般结构和颜色特性。与几种替代方法相比,我们对所选方法进行定性和定量评估。我们通过原型用户界面显示了引人注目的交互式编辑结果,该界面允许新手用户逐步完善生成的图像以匹配其所需的发型,并证明我们的方法还允许灵活且高效果的头皮合成。

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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