title: Learning Dense Hand Contact Estimation from Imbalanced Data
summary: Hands are essential to human interaction, and understanding contact between
  hands and the world can promote comprehensive understanding of their function.
  Recently, there have been growing number of hand interaction datasets that
  cover interaction with object, other hand, scene, and body. Despite the
  significance of the task and increasing high-quality data, how to effectively
  learn dense hand contact estimation remains largely underexplored. There are
  two major challenges for learning dense hand contact estimation. First, there
  exists class imbalance issue from hand contact datasets where majority of
  samples are not in contact. Second, hand contact datasets contain spatial
  imbalance issue with most of hand contact exhibited in finger tips, resulting
  in challenges for generalization towards contacts in other hand regions. To
  tackle these issues, we present a framework that learns dense HAnd COntact
  estimation (HACO) from imbalanced data. To resolve the class imbalance issue,
  we introduce balanced contact sampling, which builds and samples from multiple
  sampling groups that fairly represent diverse contact statistics for both
  contact and non-contact samples. Moreover, to address the spatial imbalance
  issue, we propose vertex-level class-balanced (VCB) loss, which incorporates
  spatially varying contact distribution by separately reweighting loss
  contribution of each vertex based on its contact frequency across dataset. As a
  result, we effectively learn to predict dense hand contact estimation with
  large-scale hand contact data without suffering from class and spatial
  imbalance issue. The codes will be released.
paper_authors._id: 682a9a3f5e6f0c59f4d8a0e5
paper_authors.name: Daniel Sungho Jung
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-19T06:42:43.502Z
paper_authors._id: 65601c6ee23401f82005e361
paper_authors.avatarUrl: /avatars/e9fc24bd8c5afd8b07a2f42765d44a7d.svg
paper_authors.fullname: Daniel Sungho Jung
paper_authors.type: user
paper_authors.user: dqj5182
paper_authors._id: 682a9a3f5e6f0c59f4d8a0e6
paper_authors.name: Kyoung Mu Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:25:48.640Z
paper_authors._id: 656056b21392aa3beb5de0bd
paper_authors.avatarUrl: /avatars/07f25b750ef308d65f2e6c82506e7816.svg
paper_authors.fullname: Kyoung Mu  Lee 
paper_authors.type: user
paper_authors.user: kyoungmu
paper_title: Learning Dense Hand Contact Estimation from Imbalanced Data
paper_summary: Hands are essential to human interaction, and understanding contact between
  hands and the world can promote comprehensive understanding of their function.
  Recently, there have been growing number of hand interaction datasets that
  cover interaction with object, other hand, scene, and body. Despite the
  significance of the task and increasing high-quality data, how to effectively
  learn dense hand contact estimation remains largely underexplored. There are
  two major challenges for learning dense hand contact estimation. First, there
  exists class imbalance issue from hand contact datasets where majority of
  samples are not in contact. Second, hand contact datasets contain spatial
  imbalance issue with most of hand contact exhibited in finger tips, resulting
  in challenges for generalization towards contacts in other hand regions. To
  tackle these issues, we present a framework that learns dense HAnd COntact
  estimation (HACO) from imbalanced data. To resolve the class imbalance issue,
  we introduce balanced contact sampling, which builds and samples from multiple
  sampling groups that fairly represent diverse contact statistics for both
  contact and non-contact samples. Moreover, to address the spatial imbalance
  issue, we propose vertex-level class-balanced (VCB) loss, which incorporates
  spatially varying contact distribution by separately reweighting loss
  contribution of each vertex based on its contact frequency across dataset. As a
  result, we effectively learn to predict dense hand contact estimation with
  large-scale hand contact data without suffering from class and spatial
  imbalance issue. The codes will be released.
paper_ai_summary: A framework addresses dense hand contact estimation from imbalanced datasets using balanced contact sampling and a vertex-level class-balanced loss to handle class and spatial imbalance issues.
paper_ai_keywords: dense hand contact estimation HACO balanced contact sampling VCB loss vertex-level class-balanced loss