紫外线补全与自我参考歧视

Jiwoo Kang, Seongmin Lee, Sanghoon Lee
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引用次数: 7

摘要

人脸UV图用于人脸重建、合成、识别和编辑等许多应用。然而,使用3D扫描设备很难收集精度所需的大量UV,或者需要多视图捕获系统来构建UV。利用最新的对准方法对三维人脸模型进行拟合后,对图像进行采样,即可得到带孔遮挡的人脸UV。本文引入一种人脸UV补全框架,利用一组不完整的UV纹理对深度神经网络进行训练。利用人脸左右半边的纹理分布几乎相等的事实,我们设计了一个对抗网络来模拟人脸纹理的完整UV分布。此外,我们还提出了一种自参考识别方案,该方案使用从生成器完成的面部UV来训练真实分布。实验证明,与使用真实紫外时相比,该网络可以在不完全紫外下完成面部纹理的训练。•计算方法→图像处理;神经网络;
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UV Completion with Self-referenced Discrimination
A facial UV map is used in many applications such as facial reconstruction, synthesis, recognition, and editing. However, it is difficult to collect a number of the UVs needed for accuracy using 3D scan device, or a multi-view capturing system should be required to construct the UV. An occluded facial UV with holes could be obtained by sampling an image after fitting a 3D facial model by recent alignment methods. In this paper, we introduce a facial UV completion framework to train the deep neural network with a set of incomplete UV textures. By using the fact that the facial texture distributions of the left and right half-sides are almost equal, we devise an adversarial network to model the complete UV distribution of the facial texture. Also, we propose the self-referenced discrimination scheme that uses the facial UV completed from the generator for training real distribution. It is demonstrated that the network can be trained to complete the facial texture with incomplete UVs comparably to when utilizing the ground-truth UVs. CCS Concepts • Computing methodologies → Image processing; Neural networks;
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