Human Face Modeling based on Deep Learning through Line-drawing

Bin Deng, Y. Kawanaka, S. Sato, K. Sakurai, Shang Gao, Z. Tang
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Abstract

This paper presents a deep learning-based method for creating 3D human face models. In recent years, several sketch-based shape modeling methods have been proposed. These methods allow the user to easily model various shapes containing animal, building, vehicle, and so on. However, a few methods have been proposed for human face models. If we can create 3D human face models via line-drawing, models of cartoon or fantasy characters can be easily created. To achieve this, we propose a sketch-based face modeling method. When a single line-drawing image is input to our system, a corresponding 3D face model are generated. Our system is based on a deep learning; many human face models and corresponding images rendered as line-drawing are prepared, and then a network is trained using these datasets. For the network, we use a previous method for reconstructing human bodies from real images, and we propose some extensions to enhance learning accuracy. Several examples are shown to demonstrate usefulness of our system.
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基于线条绘制的深度学习人脸建模
本文提出了一种基于深度学习的三维人脸模型创建方法。近年来,人们提出了几种基于草图的形状建模方法。这些方法允许用户轻松地建模各种形状,包括动物、建筑、车辆等。然而,已经提出了一些人脸模型的方法。如果我们可以通过线条绘制创建3D人脸模型,卡通或幻想人物的模型可以很容易地创建。为了实现这一目标,我们提出了一种基于草图的人脸建模方法。当一个单线绘制图像输入到我们的系统,一个相应的三维人脸模型生成。我们的系统是基于深度学习的;准备了大量的人脸模型和相应的线条绘制图像,然后使用这些数据集训练网络。对于网络,我们使用先前的方法从真实图像中重建人体,并提出了一些扩展以提高学习精度。几个例子显示了我们的系统的实用性。
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