清晰表达你的NeRF：通过条件视图合成实现无监督关节物体建模
Articulate your NeRF: Unsupervised articulated object modeling via conditional view synthesis
AI总结：
本研究提出一种无监督关节物体建模方法，通过条件视图合成从两张不同姿态的物体观测中学习部件几何、外观、分割与关节运动，采用体素初始化和解耦优化提升性能，可推广至多部件物体且少视图即可高效训练。
摘要：
我们提出了一种新颖的无监督方法，用于学习具有刚性部件的关节物体的姿态和部件分割。给定处于不同关节状态的同一物体的两张观测图像，我们的方法利用来自第一张观测图像的隐式模型学习物体部件的几何形状和外观，在渲染第二张观测图像的同时，从该图像中提取部件分割和关节运动信息。此外，为解决部件分割与关节运动联合优化的复杂性问题，我们提出了一种基于体素网格的初始化策略和一种解耦优化流程。与以往的无监督研究相比，我们的模型取得了显著更优的性能，能够推广到具有多个部件的物体，并且仅需少量视图的第二观测图像即可高效完成学习。
英文摘要：
We propose a novel unsupervised method to learn the pose and part-segmentation of articulated objects with rigid parts. Given two observations of an object in different articulation states, our method learns the geometry and appearance of object parts by using an implicit model from the first observation, distils the part segmentation and articulation from the second observation while rendering the latter observation. Additionally, to tackle the complexities in the joint optimization of part segmentation and articulation, we propose a voxel grid-based initialization strategy and a decoupled optimization procedure. Compared to the prior unsupervised work, our model obtains significantly better performance, and generalizes to objects with multiple parts while it can be efficiently from few views for the latter observation.
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。
