Understanding how humans use physical contact to interact with the world is
key to enabling human-centric artificial intelligence. While inferring 3D
contact is crucial for modeling realistic and physically-plausible human-object
interactions, existing methods either focus on 2D, consider body joints rather
than the surface, use coarse 3D body regions, or do not generalize to
in-the-wild images. In contrast, we focus on inferring dense, 3D contact
between the full body surface and objects in arbitrary images. To achieve this,
we first collect DAMON, a new dataset containing dense vertex-level contact
annotations paired with RGB images containing complex human-object and
human-scene contact. Second, we train DECO, a novel 3D contact detector that
uses both body-part-driven and scene-context-driven attention to estimate
vertex-level contact on the SMPL body. DECO builds on the insight that human
observers recognize contact by reasoning about the contacting body parts, their
proximity to scene objects, and the surrounding scene context. We perform
extensive evaluations of our detector on DAMON as well as on the RICH and
BEHAVE datasets. We significantly outperform existing SOTA methods across all
benchmarks. We also show qualitatively that DECO generalizes well to diverse
and challenging real-world human interactions in natural images. The code,
data, and models are available at this https URL

理解人类如何通过身体接触与世界互动对于实现以人为中心的人工智能至关重要。我们关注于推断全身表面与任意图像中的物体之间的密集 3D 接触，通过构建新的数据集 DAMON 以及训练新的 3D 接触检测器 DECO，在各种基准测试中显著优于现有方法，同时展示 DECO 在自然图像中广泛适用于多样且具有挑战性的真实人类互动。