title: Temporal Residual Jacobians For Rig-free Motion Transfer
summary: We introduce Temporal Residual Jacobians as a novel representation to enable
  data-driven motion transfer. Our approach does not assume access to any rigging
  or intermediate shape keyframes, produces geometrically and temporally
  consistent motions, and can be used to transfer long motion sequences. Central
  to our approach are two coupled neural networks that individually predict local
  geometric and temporal changes that are subsequently integrated, spatially and
  temporally, to produce the final animated meshes. The two networks are jointly
  trained, complement each other in producing spatial and temporal signals, and
  are supervised directly with 3D positional information. During inference, in
  the absence of keyframes, our method essentially solves a motion extrapolation
  problem. We test our setup on diverse meshes (synthetic and scanned shapes) to
  demonstrate its superiority in generating realistic and natural-looking
  animations on unseen body shapes against SoTA alternatives. Supplemental video
  and code are available at https://temporaljacobians.github.io/ .
paper_authors._id: 669f1dce384dc4fd761642c0
paper_authors.name: Sanjeev Muralikrishnan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-23T10:05:00.131Z
paper_authors._id: 669f7e451f5eece012af95a2
paper_authors.avatarUrl: /avatars/b6a148a0324041ca20efca580cec1e4d.svg
paper_authors.fullname: Sanjeev Muralikrishnan
paper_authors.type: user
paper_authors.user: sanjeevmk
paper_authors._id: 669f1dce384dc4fd761642c1
paper_authors.name: Niladri Shekhar Dutt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-23T10:05:02.122Z
paper_authors._id: 65b3e258d5bf0d622a89701b
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paper_authors.fullname: Niladri Shekhar Dutt
paper_authors.type: user
paper_authors.user: niladridutt
paper_authors._id: 669f1dce384dc4fd761642c2
paper_authors.name: Siddhartha Chaudhuri
paper_authors._id: 669f1dce384dc4fd761642c3
paper_authors.name: Noam Aigerman
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-23T10:05:03.960Z
paper_authors._id: 637ed06c1dbae09191065d0c
paper_authors.avatarUrl: /avatars/93bb8b51ca27c89b3ddce1be08f641b3.svg
paper_authors.fullname: noam
paper_authors.type: user
paper_authors.user: noamaig
paper_authors._id: 669f1dce384dc4fd761642c4
paper_authors.name: Vladimir Kim
paper_authors._id: 669f1dce384dc4fd761642c5
paper_authors.name: Matthew Fisher
paper_authors._id: 669f1dce384dc4fd761642c6
paper_authors.name: Niloy J. Mitra
paper_title: Temporal Residual Jacobians For Rig-free Motion Transfer
paper_summary: We introduce Temporal Residual Jacobians as a novel representation to enable
  data-driven motion transfer. Our approach does not assume access to any rigging
  or intermediate shape keyframes, produces geometrically and temporally
  consistent motions, and can be used to transfer long motion sequences. Central
  to our approach are two coupled neural networks that individually predict local
  geometric and temporal changes that are subsequently integrated, spatially and
  temporally, to produce the final animated meshes. The two networks are jointly
  trained, complement each other in producing spatial and temporal signals, and
  are supervised directly with 3D positional information. During inference, in
  the absence of keyframes, our method essentially solves a motion extrapolation
  problem. We test our setup on diverse meshes (synthetic and scanned shapes) to
  demonstrate its superiority in generating realistic and natural-looking
  animations on unseen body shapes against SoTA alternatives. Supplemental video
  and code are available at https://temporaljacobians.github.io/ .
paper_ai_summary: Temporal Residual Jacobians enable data-driven motion transfer without keyframes by integrating local geometric and temporal changes into realistic animations.
paper_ai_keywords: Temporal Residual Jacobians neural networks 3D positional information motion extrapolation realistic animations