Despite many attempts in the last few years, automatic analysis of social scenes captured by wide-angle camera networks remains a very challenging task due to the low resolution of targets, background clutter and frequent and persistent occlusions. In this paper, we present a novel framework for jointly estimating (i) head, body orientations of targets and (ii) conversational groups called F-formations from social scenes. In contrast to prior works that have (a) exploited the limited range of head and body orientations to jointly learn both, or (b) employed the mutual head (but not body) pose of interactors for deducing F-formations, we propose a weakly-supervised learning algorithm for joint inference. Our algorithm employs body pose as the primary cue for F-formation estimation, and an alternating optimization strategy is proposed to iteratively refine F-formation and pose estimates. We demonstrate the increased efficacy of joint inference over the state-of-the-art via extensive experiments on three social datasets.

中文翻译：

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从社交场景中联合估计人体姿势和会话组

尽管在过去几年中进行了多次尝试，但由于目标分辨率低、背景杂乱以及频繁和持续的遮挡，广角摄像头网络捕获的社交场景的自动分析仍然是一项非常具有挑战性的任务。在本文中，我们提出了一个新的框架，用于联合估计（i）目标的头部、身体方向和（ii）来自社交场景的称为 F-formations 的对话组。与之前的工作（a）利用有限范围的头部和身体方向来共同学习两者，或（b）利用交互者的相互头部（但不是身体）姿势来推断 F 形成，我们提出了一个弱-用于联合推理的监督学习算法。我们的算法采用身体姿势作为 F 形成估计的主要线索，并提出了一种交替优化策略来迭代改进 F 形成和姿态估计。我们通过对三个社交数据集的广泛实验证明了联合推理对最先进技术的更高效率。