title: LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
summary: 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.
paper_authors._id: 66e1a074ac5c2cb8fa9ab384
paper_authors.name: Archana Swaminathan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-11T15:26:36.056Z
paper_authors._id: 64baae8e94c0e3be4aa2cd0d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64baae8e94c0e3be4aa2cd0d/X64nbllxw3Vn_uy4j0qxY.jpeg
paper_authors.fullname: Archana Swaminathan
paper_authors.type: user
paper_authors.user: archana14
paper_authors._id: 66e1a074ac5c2cb8fa9ab385
paper_authors.name: Anubhav Gupta
paper_authors._id: 66e1a074ac5c2cb8fa9ab386
paper_authors.name: Kamal Gupta
paper_authors._id: 66e1a074ac5c2cb8fa9ab387
paper_authors.name: Shishira R. Maiya
paper_authors._id: 66e1a074ac5c2cb8fa9ab388
paper_authors.name: Vatsal Agarwal
paper_authors._id: 66e1a074ac5c2cb8fa9ab389
paper_authors.name: Abhinav Shrivastava
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-11T15:27:25.063Z
paper_authors._id: 64b683acb20146a0d307222c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/l665j1WeROwhQKgIsszYp.jpeg
paper_authors.fullname: Abhinav Shrivastava
paper_authors.type: user
paper_authors.user: abhi2610
paper_title: LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
paper_summary: 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.
paper_ai_keywords: Neural Radiance Fields (NeRFs) 3D reconstruction dynamic objects object articulations part-level reconstruction motion estimation hypernetwork view-invariant latent representation novel articulation configurations articulation registration