让3D动态物体在任意视角下都能自然变形，无需预设关节数量。
LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
- 用状态条件超网络，学习每个姿态的视角无关隐空间表示。
- 通过状态插值生成未见过的新关节配置，重建精度优于基线方法。
- 适合需要灵活建模复杂运动物体的科研与工业场景。
神经辐射场（NeRF）在静态3D场景和物体重建中取得了突破性进展，但将其拓展至动态物体或关节运动仍具挑战。现有方法依赖于对运动部件数量或物体类别的启发式假设，限制了实用性。本文提出LEIA，一种新型动态3D物体表征方法：通过观察物体在不同时间状态下的图像，以当前状态为条件输入超网络，参数化其对应的NeRF；该方法可学习每个状态的视角不变隐表示。进一步实验表明，通过在状态间插值，可生成此前未见的3D关节配置。结果验证了该方法在视角无关且不依赖关节配置下的有效性和优越性，尤其在基于运动信息进行关节注册的任务中显著超越先前方法。
原文摘要 · Abstract (English)
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.
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