论文标题
通过合成数据集创建管道增强了3DMM属性控制
Enhanced 3DMM Attribute Control via Synthetic Dataset Creation Pipeline
论文作者
论文摘要
尽管由于其许多实际用途，对2D图像的面部属性操纵通过生成对抗网络（GAN）在计算机视觉和图形中变得很普遍，但对3D属性操作的研究相对尚未开发。现有的3D属性操纵方法受到限制，因为将相同的语义更改应用于每个3D面。开发更好的3D属性控制方法的关键挑战是缺乏配对训练数据，其中一个属性会更改，而其他属性则固定了 - 例如，一对3D面是男性，另一个是女性，但所有其他属性，例如种族和表达，例如种族和表达。为了克服这一挑战，我们设计了一条新颖的管道，用于利用gan的力量来产生配对的3D面。除了这条管道之外，我们然后提出了一个增强的非线性3D条件属性控制器，与现有方法相比，增加了3D属性控制的精度和多样性。我们通过定量和定性评估证明了数据集创建管道的有效性以及条件属性控制器的出色性能。
While facial attribute manipulation of 2D images via Generative Adversarial Networks (GANs) has become common in computer vision and graphics due to its many practical uses, research on 3D attribute manipulation is relatively undeveloped. Existing 3D attribute manipulation methods are limited because the same semantic changes are applied to every 3D face. The key challenge for developing better 3D attribute control methods is the lack of paired training data in which one attribute is changed while other attributes are held fixed -- e.g., a pair of 3D faces where one is male and the other is female but all other attributes, such as race and expression, are the same. To overcome this challenge, we design a novel pipeline for generating paired 3D faces by harnessing the power of GANs. On top of this pipeline, we then propose an enhanced non-linear 3D conditional attribute controller that increases the precision and diversity of 3D attribute control compared to existing methods. We demonstrate the validity of our dataset creation pipeline and the superior performance of our conditional attribute controller via quantitative and qualitative evaluations.
