Expressive line drawings of human faces from range images
Expressive line drawings of human faces from range images
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DOI：
10.1007/s11432-009-0039-3
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发表时间：
2009-01
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影响因子：
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通讯作者：
Yuezhu Huang;Ralph Robert Martin;Paul L. Rosin;Xiangxu Meng;Chenglei Yang
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文献类型：
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作者：
Yuezhu Huang;Ralph Robert Martin;Paul L. Rosin;Xiangxu Meng;Chenglei Yang
我们提出了一种新的技术，从范围图像中提取特征，并使用它们来产生一个3D钢笔和墨水风格的肖像类似于传统的艺术绘画。与大多数以前的基于模板，基于组件或基于示例的人脸素描方法，从正面照片作为输入，我们的系统使用范围图像作为输入。我们的方法对于中等复杂度的模型实时运行，允许交互式修改姿势和绘制风格。肖像画在我们的系统中使用的闭塞轮廓和暗示轮廓作为最重要的形状线索。然而，当前的3D特征线检测方法需要平滑的网格，并且不能可靠地直接应用于有噪声的距离图像。因此，我们提出了一种改进的轮廓线检测算法。从范围图像中提取与面部重要部分相关的特征边缘，连接并平滑，使我们能够构建线路径链，然后可以根据需要进行渲染。我们还结合了各种肖像画的原则，提供了几个简单而有效的非真实感肖像渲染，如钢笔和墨水着色器，阴影着色器和草图着色器。这些能够从用户选择的角度以不同的风格产生各种逼真的印象。为了获得令人满意的结果，我们通过平滑线条粗细和不透明度的变化来细化渲染输出。我们谨慎地提供适当的视觉线索，以提高观众对人脸的理解。实验结果证明了该方法的鲁棒性和有效性，并进一步表明该方法可以扩展到其他3D几何对象。
We propose a novel technique to extract features from a range image and use them to produce a 3D pen-and-ink style portrait similar to a traditional artistic drawing. Unlike most previous template-based, component-based or example-based face sketching methods, which work from a frontal photograph as input, our system uses a range image as input. Our method runs in real-time for models of moderate complexity, allowing the pose and drawing style to be modified interactively. Portrait drawing in our system makes use of occluding contours and suggestive contours as the most important shape cues. However, current 3D feature line detection methods require a smooth mesh and cannot be reliably applied directly to noisy range images. We thus present an improved silhouette line detection algorithm. Feature edges related to the significant parts of a face are extracted from the range image, connected, and smoothed, allowing us to construct chains of line paths which can then be rendered as desired. We also incorporate various portrait-drawing principles to provide several simple yet effective nonphotorealistic portrait renderers such as a pen-and-ink shader, a hatch shader and a sketch shader. These are able to generate various life-like impressions in different styles from a user-chosen viewpoint. To obtain satisfactory results, we refine rendered output by smoothing changes in line thickness and opacity. We are careful to provide appropriate visual cues to enhance the viewer’s comprehension of the human face. Our experimental results demonstrate the robustness and effectiveness of our approach, and further suggest that our approach can be extended to other 3D geometric objects.