发表机构
University of Birmingham; Beijing Institute of Technology（伯明翰大学; 北京理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出VolCo体积接触表示及VolCoDiff框架，通过三维网格编码接触并分层建模，显著减少手物穿透，实现高保真抓取生成。
AI 中文摘要
准确的接触建模对于理解手-物体交互至关重要，然而现有的接触表示通常局限于物体表面，并依赖手工设计的规则来恢复接触细节，导致严重的穿透和不合理的结果。为了更好地利用动作捕捉数据中的丰富细节，我们引入了体积接触（VolCo），这是一种将表面点扩展为一组三维体积网格的表示方法。VolCo编码三维接触，使得手部部件的精确恢复成为可能，并且其组织具有固有的层次结构：每个体积内的局部接触细节和跨所有体积的全局手部几何。我们的框架VolCoDiff采用两个模块来遵循这一层次结构捕获局部和全局特征。对于局部接触细节，我们使用三维变分自编码器来建模以局部物体符号距离场（SDF）为条件的手部可能配置。对于全局手部几何，我们设计了一个先验引导的扩散模型，该模型学习从体积网格聚合的压缩潜在特征的分布。我们在两个基准数据集上评估了我们的方法，并在穿透和稳定性方面展示了最先进的性能，表明能够生成穿透严重程度大大降低的紧密抓取。我们的代码可在该https URL获取。
英文摘要
Accurate contact modeling is fundamental to understanding hand-object interaction, yet existing contact representations are typically restricted to object surfaces and rely on hand-crafted rules to recover contact details, leading to severe penetrations and implausible results. To better exploit the rich detail in motion-capture data, we introduce Volumetric Contact (VolCo), a representation that expands surface points to a set of 3D volumetric grids. VolCo encodes 3D contact that allows precise hand part recovery, and is organized in an inherent hierarchy: local contact details within each volume and global hand geometry across all volumes. Our framework, VolCoDiff, employs two modules to capture local and global features following this hierarchy. For local contact details, we use a 3D variational autoencoder to model the possible hand configurations conditioned on the local object signed distance field (SDF). For global hand geometry, we design a prior-guided diffusion model that learns the distribution of compressed latent features aggregated from the volumetric grids. We evaluate our method on two benchmark datasets and demonstrate state-of-the-art performance in penetration and stability, indicating the capability to generate tight grasps with much less severe penetrations. Our code is available at https://github.com/chzh9311/volco.
CommentsAccepted to NeurIPS 2026
