LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
Authors: name: Archana Swaminathan, name: Anubhav Gupta, name: Kamal Gupta, name: Shishira R. Maiya, name: Vatsal Agarwal, name: Abhinav Shrivastava

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.