Recent years have witnessed significant progress in the field of neural surface reconstruction. While the extensive focus was put on volumetric and implicit approaches, a number of works have shown that explicit graphics primitives such as point clouds can significantly reduce computational complexity, without sacrificing the reconstructed surface quality. However, less emphasis has been put on modeling dynamic surfaces with point primitives. In this work, we present a dynamic point field model that combines the representational benefits of explicit point-based graphics with implicit deformation networks to allow efficient modeling of non-rigid 3D surfaces. Using explicit surface primitives also allows us to easily incorporate well-established constraints such as-isometric-as-possible regularisation. While learning this deformation model is prone to local optima when trained in a fully unsupervised manner, we propose to additionally leverage semantic information such as keypoint dynamics to guide the deformation learning. We demonstrate our model with an example application of creating an expressive animatable human avatar from a collection of 3D scans. Here, previous methods mostly rely on variants of the linear blend skinning paradigm, which fundamentally limits the expressivity of such models when dealing with complex cloth appearances such as long skirts. We show the advantages of our dynamic point field framework in terms of its representational power, learning efficiency, and robustness to out-of-distribution novel poses.

翻译：近年来，神经表面重建领域取得了显著进展。尽管大量研究聚焦于体积和隐式方法，但一些研究表明，点云等显式图形基元可在不牺牲重建表面质量的前提下显著降低计算复杂度。然而，利用点基元建模动态表面的研究仍相对不足。本文提出一种动态点场模型，该模型结合显式点基元图形的表征优势与隐式变形网络，实现对非刚性三维表面的高效建模。通过使用显式表面基元，我们能够轻松融入诸如尽可能等距正则化等成熟约束。由于该变形模型在完全无监督训练时易陷入局部最优，我们进一步提出利用关键点动力学等语义信息引导变形学习。我们以从三维扫描集合中创建可驱动的人体化身为例验证模型效果：传统方法大多依赖线性混合蒙皮范式及其变体，这在处理长裙等复杂衣物外观时从根本上限制了模型的表达能力。实验表明，我们的动态点场框架在表征能力、学习效率及对分布外新姿态的鲁棒性方面均具有显著优势。