NPMs：三维可变形神经网络的神经参数模型
NPMs: Neural Parametric Models for 3D Deformable Shapes
摘要：
参数化3D模型已经在计算机图形和视觉中实现了各种各样的任务，例如对人体、面部和手部进行建模。然而，这些参数化模型的构建通常是繁琐的，因为它需要大量的手动调整，并且它们难以表示额外的复杂性和细节，例如皱纹或衣服。为此，我们提出了神经参数模型（NPMs），这是一种新颖的、学习的替代传统参数化3D模型的方法，它不需要手工制作的特定于对象的约束。特别是，我们学习解开4D动态到形状和姿势的潜在空间表示，利用最近的发展，在学习隐式功能的灵活性。至关重要的是，一旦学习，我们的形状和姿势的神经参数模型能够在学习的空间上进行优化，以适应新的观察，类似于传统参数模型的拟合，例如，SMPL。这使得NPM能够实现观察到的可变形序列的显著更准确和详细的表示。我们表明，NPMs显着改善了参数和非参数的最先进的重建和跟踪的单眼深度序列的穿着的人和手。潜空间插值以及形状/姿态转移实验进一步证明了NPM的实用性。代码可在https://pablopalafox.github.io/npms上公开获取。
英文摘要：
Parametric 3D models have enabled a wide variety of tasks in computer graphics and vision, such as modeling human bodies, faces, and hands. However, the construction of these parametric models is often tedious, as it requires heavy manual tweaking, and they struggle to represent additional complexity and details such as wrinkles or clothing. To this end, we propose Neural Parametric Models (NPMs), a novel, learned alternative to traditional, parametric 3D models, which does not require hand-crafted, object-specific constraints. In particular, we learn to disentangle 4D dynamics into latent-space representations of shape and pose, leveraging the flexibility of recent developments in learned implicit functions. Crucially, once learned, our neural parametric models of shape and pose enable optimization over the learned spaces to fit to new observations, similar to the fitting of a traditional parametric model, e.g., SMPL. This enables NPMs to achieve a significantly more accurate and detailed representation of observed deformable sequences. We show that NPMs improve notably over both parametric and non-parametric state of the art in reconstruction and tracking of monocular depth sequences of clothed humans and hands. Latent-space interpolation as well as shape/pose transfer experiments further demonstrate the usefulness of NPMs. Code is publicly available at https://pablopalafox.github.io/npms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
