We propose an approach for reconstructing free-moving object from a monocular
RGB video. Most existing methods either assume scene prior, hand pose prior,
object category pose prior, or rely on local optimization with multiple
sequence segments. We propose a method that allows free interaction with the
object in front of a moving camera without relying on any prior, and optimizes
the sequence globally without any segments. We progressively optimize the
object shape and pose simultaneously based on an implicit neural
representation. A key aspect of our method is a virtual camera system that
reduces the search space of the optimization significantly. We evaluate our
method on the standard HO3D dataset and a collection of egocentric RGB
sequences captured with a head-mounted device. We demonstrate that our approach
outperforms most methods significantly, and is on par with recent techniques
that assume prior information.

我们提出了一种从单目 RGB 视频中重建自由移动物体的方法，无需任何先验信息，通过基于隐式神经表示同时优化对象的形状和姿态，其中方法的关键是一个虚拟摄像机系统，显著减小了优化的搜索空间。通过在 HO3D 数据集和佩戴在头部设备上捕获的主观 RGB 序列上的评估，我们证明了该方法明显优于大多数方法，并且与假设先验信息的最新技术相媲美。