LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
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.
66e1a074ac5c2cb8fa9ab384
Archana Swaminathan
admin_assigned
2024-09-11T15:26:36.056Z
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Archana Swaminathan
user
archana14
66e1a074ac5c2cb8fa9ab385
Anubhav Gupta
66e1a074ac5c2cb8fa9ab386
Kamal Gupta
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Shishira R. Maiya
66e1a074ac5c2cb8fa9ab388
Vatsal Agarwal
claimed_verified
2025-07-10T07:10:31.949Z
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Vatsal Agarwal
user
vatsalag
66e1a074ac5c2cb8fa9ab389
Abhinav Shrivastava
admin_assigned
2024-09-11T15:27:25.063Z
64b683acb20146a0d307222c
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/l665j1WeROwhQKgIsszYp.jpeg
Abhinav Shrivastava
user
abhi2610
LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
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.
LEIA learns view-invariant latent representations for dynamic 3D objects using hypernetworks to model different states and generate novel articulations.
Neural Radiance Fields NeRFs dynamic objects object articulations part-level reconstruction motion estimation hypernetworks view-invariant latent representation articulation registration