LeGO：利用表面变形网络基于单个示例生成可动画的程式化人脸
LeGO: Leveraging a Surface Deformation Network for Animatable Stylized Face Generation with One Example
AI总结：
提出基于表面变形网络与MAGE编码器的方法，利用可微渲染和方向性CLIP损失实现高度可动画的3D人脸程式化生成，支持多样拓扑结构输出。
摘要：
3D人脸程式化生成的最新进展在少样本到零样本设置中取得了显著进展。然而，现有方法实现的程式化程度通常不足以满足实际应用，因为它们大多基于变化有限的统计3D可变形模型（3DMM）。为此，我们提出了一种能够生成具有所需拓扑结构的高度程式化3D人脸模型的方法。我们的方法使用3DMM训练表面变形网络，并利用配对示例将其域转换为目标风格。该网络通过使用可微渲染器和方向性CLIP损失模仿目标风格，实现3D人脸网格的程式化。此外，在推理过程中，我们利用网格无关编码器（MAGE），将程式化过程中的变形目标（即具有多样拓扑结构的网格）作为输入，并将其形状编码到我们的潜在空间中。生成的程式化人脸模型可以通过常用的3DMM混合变形进行动画处理。一系列定量和定性评估表明，我们的方法能够根据给定风格生成高度程式化的人脸网格，并以所需拓扑结构输出。我们还展示了该方法的示例应用，包括基于图像的程式化虚拟形象生成、几何风格的线性插值以及程式化虚拟形象的面部动画。
英文摘要：
Recent advances in 3D face stylization have made significant strides in few to zero-shot settings. However, the degree of stylization achieved by existing methods is often not sufficient for practical applications because they are mostly based on statistical 3D Morphable Models (3DMM) with limited variations. To this end, we propose a method that can produce a highly stylized 3D face model with desired topology. Our methods train a surface deformation network with 3DMM and translate its domain to the target style using a paired exemplar. The network achieves stylization of the 3D face mesh by mimicking the style of the target using a differentiable renderer and directional CLIP losses. Additionally, during the inference process, we utilize a Mesh Agnostic Encoder (MAGE) that takes deformation target, a mesh of diverse topologies as input to the stylization process and encodes its shape into our latent space. The resulting stylized face model can be animated by commonly used 3DMM blend shapes. A set of quantitative and qualitative evaluations demonstrate that our method can produce highly stylized face meshes according to a given style and output them in a desired topology. We also demonstrate example applications of our method including image-based stylized avatar generation, linear interpolation of geometric styles, and facial animation of stylized avatars.
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