Social interaction is a fundamental aspect of human behavior and
communication. The way individuals position themselves in relation to others,
also known as proxemics, conveys social cues and affects the dynamics of social
interaction. We present a novel approach that learns a 3D proxemics prior of
two people in close social interaction. Since collecting a large 3D dataset of
interacting people is a challenge, we rely on 2D image collections where social
interactions are abundant. We achieve this by reconstructing pseudo-ground
truth 3D meshes of interacting people from images with an optimization approach
using existing ground-truth contact maps. We then model the proxemics using a
novel denoising diffusion model called BUDDI that learns the joint distribution
of two people in close social interaction directly in the SMPL-X parameter
space. Sampling from our generative proxemics model produces realistic 3D human
interactions, which we validate through a user study. Additionally, we
introduce a new optimization method that uses the diffusion prior to
reconstruct two people in close proximity from a single image without any
contact annotation. Our approach recovers more accurate and plausible 3D social
interactions from noisy initial estimates and outperforms state-of-the-art
methods. See our project site for code, data, and model:
muelea.github.io/buddi.

本文通过一种基于 2D 图像的方法学习了二人社交互动的 3D proxemics 数据，介绍了一种新的去噪扩散模型 BUDDI 来可视化 3D 人际互动情境，并利用新的优化方法从单张图像中重构两个人的社交关系，其结果相较于现有方法更为准确和合理。