Accurately estimating the 3D pose of the camera wearer in egocentric video sequences is crucial to modeling human behavior in virtual and augmented reality applications. The task presents unique challenges due to the limited visibility of the user's body caused by the front-facing camera mounted on their head. Recent research has explored the utilization of the scene and ego-motion, but it has overlooked humans' interactive nature. We propose a novel framework for Social Egocentric Estimation of body MEshes (SEE-ME). Our approach is the first to estimate the wearer's mesh using only a latent probabilistic diffusion model, which we condition on the scene and, for the first time, on the social wearer-interactee interactions. Our in-depth study sheds light on when social interaction matters most for ego-mesh estimation; it quantifies the impact of interpersonal distance and gaze direction. Overall, SEE-ME surpasses the current best technique, reducing the pose estimation error (MPJPE) by 53%. The code is available at https://github.com/L-Scofano/SEEME.

本研究解决了在以自我为中心的视频序列中准确估计佩戴者的3D姿态这一问题。论文提出了一种新框架SEE-ME，通过潜在的概率扩散模型结合场景和社会互动，首次实现基于互动信息的网格估计。研究结果显示，SEE-ME在减少姿态估计误差方面超过了现有最佳技术达53%。

社会自我网格估计