title: GeneOH Diffusion: Towards Generalizable Hand-Object Interaction
    Denoising via Denoising Diffusion
summary: In this work, we tackle the challenging problem of denoising hand-object
  interactions (HOI). Given an erroneous interaction sequence, the objective is
  to refine the incorrect hand trajectory to remove interaction artifacts for a
  perceptually realistic sequence. This challenge involves intricate interaction
  noise, including unnatural hand poses and incorrect hand-object relations,
  alongside the necessity for robust generalization to new interactions and
  diverse noise patterns. We tackle those challenges through a novel approach,
  GeneOH Diffusion, incorporating two key designs: an innovative contact-centric
  HOI representation named GeneOH and a new domain-generalizable denoising
  scheme. The contact-centric representation GeneOH informatively parameterizes
  the HOI process, facilitating enhanced generalization across various HOI
  scenarios. The new denoising scheme consists of a canonical denoising model
  trained to project noisy data samples from a whitened noise space to a clean
  data manifold and a "denoising via diffusion" strategy which can handle input
  trajectories with various noise patterns by first diffusing them to align with
  the whitened noise space and cleaning via the canonical denoiser. Extensive
  experiments on four benchmarks with significant domain variations demonstrate
  the superior effectiveness of our method. GeneOH Diffusion also shows promise
  for various downstream applications. Project website:
  https://meowuu7.github.io/GeneOH-Diffusion/.
paper_authors._id: 65d82658bc9081f6c904e035
paper_authors.name: Xueyi Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-23T13:08:50.028Z
paper_authors._id: 65b8070ad49f4330ab0ca5f7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/t4fI-3djMfgXCchU_xpjL.png
paper_authors.fullname: Xueyi Liu
paper_authors.type: user
paper_authors.user: xymeow7
paper_authors._id: 65d82658bc9081f6c904e036
paper_authors.name: Li Yi
paper_title: GeneOH Diffusion: Towards Generalizable Hand-Object Interaction
    Denoising via Denoising Diffusion
paper_summary: In this work, we tackle the challenging problem of denoising hand-object
  interactions (HOI). Given an erroneous interaction sequence, the objective is
  to refine the incorrect hand trajectory to remove interaction artifacts for a
  perceptually realistic sequence. This challenge involves intricate interaction
  noise, including unnatural hand poses and incorrect hand-object relations,
  alongside the necessity for robust generalization to new interactions and
  diverse noise patterns. We tackle those challenges through a novel approach,
  GeneOH Diffusion, incorporating two key designs: an innovative contact-centric
  HOI representation named GeneOH and a new domain-generalizable denoising
  scheme. The contact-centric representation GeneOH informatively parameterizes
  the HOI process, facilitating enhanced generalization across various HOI
  scenarios. The new denoising scheme consists of a canonical denoising model
  trained to project noisy data samples from a whitened noise space to a clean
  data manifold and a "denoising via diffusion" strategy which can handle input
  trajectories with various noise patterns by first diffusing them to align with
  the whitened noise space and cleaning via the canonical denoiser. Extensive
  experiments on four benchmarks with significant domain variations demonstrate
  the superior effectiveness of our method. GeneOH Diffusion also shows promise
  for various downstream applications. Project website:
  https://meowuu7.github.io/GeneOH-Diffusion/.
paper_ai_keywords: GeneOH Diffusion contact-centric HOI representation GeneOH domain-generalizable denoising canonical denoising model whitened noise space clean data manifold denoising via diffusion