Reconstructing 3D Human-Object Interaction from an RGB image is essential for perceptive systems. Yet, this remains challenging as it requires capturing the subtle physical coupling between the body and objects. While current methods rely on sparse, binary contact cues, these fail to model the continuous proximity and dense spatial relationships that characterize natural interactions. We address this limitation via InterFields, a representation that encodes dense, continuous proximity across the entire body and object surfaces. However, inferring these fields from single images is inherently ill-posed. To tackle this, our intuition is that interaction patterns are characteristically structured by the action and object geometry. We capture this structure in LEXIS, a novel discrete manifold of interaction signatures learned via a VQ-VAE. We then develop LEXIS-Flow, a diffusion framework that leverages LEXIS signatures to estimate human and object meshes alongside their InterFields. Notably, these InterFields help in a guided refinement that ensures physically-plausible, proximity-aware reconstructions without requiring post-hoc optimization. Evaluation on Open3DHOI and BEHAVE shows that LEXIS-Flow significantly outperforms existing SotA baselines in reconstruction, contact, and proximity quality. Our approach not only improves generalization but also yields reconstructions perceived as more realistic, moving us closer to holistic 3D scene understanding. Code & models will be public at https://anticdimi.github.io/lexis.

翻译：[translated abstract in Chinese]: 从RGB图像重建三维人-物交互对于感知系统至关重要。然而，由于需要捕捉人体与物体之间微妙的物理耦合，这一任务仍具挑战性。当前方法依赖稀疏的二元接触线索，无法建模自然交互中表征的连续邻近关系和密集空间关联。我们通过InterFields这一表示（编码全身和物体表面密集连续的邻近关系）来解决上述局限性。但单张图像推断这些场本质上是病态的。为此，我们基于交互模式由动作和物体几何结构特征性组织的直觉，在LEXIS中通过VQ-VAE学习新颖的离散交互签名流形。进而开发LEXIS-Flow扩散框架，利用LEXIS签名估计人体与物体网格及其InterFields。值得注意的是，这些InterFields在引导细化中发挥作用，无需后处理优化即可确保符合物理规律且感知邻近关系的重建结果。在Open3DHOI和BEHAVE上的评估表明，LEXIS-Flow在重建质量、接触质量和邻近质量方面显著优于现有最优基线。我们的方法不仅提升了泛化能力，还生成了感知更真实的重建结果，推动我们对三维场景的整体理解。代码与模型将公开于https://anticdimi.github.io/lexis。