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/.

翻译：本文旨在解决手-物交互去噪这一挑战性问题。给定存在误差的交互序列，目标是修正错误的手部轨迹以消除交互伪影，从而生成感知上真实的序列。该挑战涉及复杂交互噪声，包括不自然的手部姿态及错误的手-物关系，同时要求对新交互场景和多样化噪声模式具备稳健的泛化能力。我们通过提出名为GeneOH Diffusion的创新方法应对上述挑战，该方法包含两项核心设计：一是以接触为中心的创新性HOI表示——GeneOH，二是新的领域可泛化去噪方案。以接触为中心的表示方法GeneOH通过参数化方式有效描述HOI过程，有助于增强跨多种HOI场景的泛化能力。新去噪方案包含一个标准去噪模型（用于将白化噪声空间中的含噪数据样本投影至干净数据流形）以及一种"基于扩散的去噪"策略（该策略首先将不同噪声模式的输入轨迹扩散至白化噪声空间以对齐分布，再通过标准去噪模型进行清理）。在四个具有显著领域差异的基准数据集上的大量实验证明了我们方法的优越有效性。GeneOH Diffusion在多种下游应用中也展现出广阔前景。项目网站：https://meowuu7.github.io/GeneOH-Diffusion/。