论文标题
imface：具有隐式神经表示的非线性3D可变形面模型
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
论文作者
论文摘要
3D面的精确表示对各种计算机视觉和图形应用都有益。但是，由于数据离散和模型线性性，在当前研究中捕获准确的身份和表达线索仍然具有挑战性。本文提出了一种新颖的3D形式的面部模型，即Imface，以学习具有隐式神经表示的非线性和连续空间。它构建了两个明确的分解的变形字段，以分别建模与身份和表达式相关的复杂形状，并设计一种改进的学习策略，以扩展表达式的嵌入，以允许更加多样化的更改。我们进一步介绍了一个神经混合场，通过自适应融合一系列本地领域来学习复杂的细节。除了IMFACE外，还提出了有效的预处理管道，以解决隐性表示中的水密输入要求问题，使它们能够首次与常见的面部表面一起工作。进行广泛的实验以证明IMFACE的优势。
Precise representations of 3D faces are beneficial to various computer vision and graphics applications. Due to the data discretization and model linearity, however, it remains challenging to capture accurate identity and expression clues in current studies. This paper presents a novel 3D morphable face model, namely ImFace, to learn a nonlinear and continuous space with implicit neural representations. It builds two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, and designs an improved learning strategy to extend embeddings of expressions to allow more diverse changes. We further introduce a Neural Blend-Field to learn sophisticated details by adaptively blending a series of local fields. In addition to ImFace, an effective preprocessing pipeline is proposed to address the issue of watertight input requirement in implicit representations, enabling them to work with common facial surfaces for the first time. Extensive experiments are performed to demonstrate the superiority of ImFace.
