Neural Radiance Fields (NeRFs) have revolutionized the reconstruction of static scenes and objects in 3D, offering unprecedented quality. However, extending NeRFs to model dynamic objects or object articulations remains a challenging problem. Previous works have tackled this issue by focusing on part-level reconstruction and motion estimation for objects, but they often rely on heuristics regarding the number of moving parts or object categories, which can limit their practical use. In this work, we introduce LEIA, a novel approach for representing dynamic 3D objects. Our method involves observing the object at distinct time steps or "states" and conditioning a hypernetwork on the current state, using this to parameterize our NeRF. This approach allows us to learn a view-invariant latent representation for each state. We further demonstrate that by interpolating between these states, we can generate novel articulation configurations in 3D space that were previously unseen. Our experimental results highlight the effectiveness of our method in articulating objects in a manner that is independent of the viewing angle and joint configuration. Notably, our approach outperforms previous methods that rely on motion information for articulation registration.

本研究针对动态物体建模中的挑战，提出了一种新颖的方法LEIA，利用不同时间状态下的观察条件化超网络，从而为每个状态学习视图不变的潜在表示。实验结果表明，该方法能够高效生成新颖的3D关节配置，且不受视角和关节配置的影响，显著优于依赖运动信息的先前方法。

LEIA：用于隐式3D关节动作的潜在视图不变嵌入