title: WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds
summary: We present a new approach for understanding the periodicity structure and
  semantics of motion datasets, independently of the morphology and skeletal
  structure of characters. Unlike existing methods using an overly sparse
  high-dimensional latent, we propose a phase manifold consisting of multiple
  closed curves, each corresponding to a latent amplitude. With our proposed
  vector quantized periodic autoencoder, we learn a shared phase manifold for
  multiple characters, such as a human and a dog, without any supervision. This
  is achieved by exploiting the discrete structure and a shallow network as
  bottlenecks, such that semantically similar motions are clustered into the same
  curve of the manifold, and the motions within the same component are aligned
  temporally by the phase variable. In combination with an improved motion
  matching framework, we demonstrate the manifold's capability of timing and
  semantics alignment in several applications, including motion retrieval,
  transfer and stylization. Code and pre-trained models for this paper are
  available at https://peizhuoli.github.io/walkthedog.
paper_authors._id: 66a8512d45f5642d5f02f8b1
paper_authors.name: Peizhuo Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-30T13:46:22.874Z
paper_authors._id: 66a8e7d4a203add9773fe968
paper_authors.avatarUrl: /avatars/0f42c55a02aef4bcca64f774fbd23c15.svg
paper_authors.fullname: Peizhuo Li
paper_authors.type: user
paper_authors.user: PeizhuoL
paper_authors._id: 66a8512d45f5642d5f02f8b2
paper_authors.name: Sebastian Starke
paper_authors._id: 66a8512d45f5642d5f02f8b3
paper_authors.name: Yuting Ye
paper_authors._id: 66a8512d45f5642d5f02f8b4
paper_authors.name: Olga Sorkine-Hornung
paper_title: WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds
paper_summary: We present a new approach for understanding the periodicity structure and
  semantics of motion datasets, independently of the morphology and skeletal
  structure of characters. Unlike existing methods using an overly sparse
  high-dimensional latent, we propose a phase manifold consisting of multiple
  closed curves, each corresponding to a latent amplitude. With our proposed
  vector quantized periodic autoencoder, we learn a shared phase manifold for
  multiple characters, such as a human and a dog, without any supervision. This
  is achieved by exploiting the discrete structure and a shallow network as
  bottlenecks, such that semantically similar motions are clustered into the same
  curve of the manifold, and the motions within the same component are aligned
  temporally by the phase variable. In combination with an improved motion
  matching framework, we demonstrate the manifold's capability of timing and
  semantics alignment in several applications, including motion retrieval,
  transfer and stylization. Code and pre-trained models for this paper are
  available at https://peizhuoli.github.io/walkthedog.
paper_ai_summary: A vector quantized periodic autoencoder learns a shared phase manifold for character motion, enabling semantic and temporal alignment in various applications.
paper_ai_keywords: phase manifold vector quantized periodic autoencoder latent amplitude shallow network motion retrieval motion transfer motion stylization