论文标题
NDF：动态人类建模的神经可变形场
NDF: Neural Deformable Fields for Dynamic Human Modelling
论文作者
论文摘要
我们提出了神经可变形场（NDF），这是一种从多视频视频中进行动态人类数字化的新表示形式。最近的作品提出，代表一个具有共同规范神经辐射场的动态人体，该范围与变形场估计与观察空间联系起来。但是，学到的规范表示是静态的，并且变形场的当前设计无法代表大型运动或详细的几何变化。在本文中，我们建议学习一个围绕合适的参数体模型包裹的神经可变形场，以代表动态人体。 NDF通过基础参考表面在空间上对齐。然后，学会了神经网络将姿势映射到NDF的动力学。提出的NDF表示可以通过新颖的观点和新颖的姿势合成数字化的表演者，并具有详细且合理的动态外观。实验表明，我们的方法显着胜过最近的人类合成方法。
We propose Neural Deformable Fields (NDF), a new representation for dynamic human digitization from a multi-view video. Recent works proposed to represent a dynamic human body with shared canonical neural radiance fields which links to the observation space with deformation fields estimations. However, the learned canonical representation is static and the current design of the deformation fields is not able to represent large movements or detailed geometry changes. In this paper, we propose to learn a neural deformable field wrapped around a fitted parametric body model to represent the dynamic human. The NDF is spatially aligned by the underlying reference surface. A neural network is then learned to map pose to the dynamics of NDF. The proposed NDF representation can synthesize the digitized performer with novel views and novel poses with a detailed and reasonable dynamic appearance. Experiments show that our method significantly outperforms recent human synthesis methods.
