Human silhouette extraction is a fundamental task in computer vision with applications in various downstream tasks. However, occlusions pose a significant challenge, leading to incomplete and distorted silhouettes. To address this challenge, we introduce POISE: Pose Guided Human Silhouette Extraction under Occlusions, a novel self-supervised fusion framework that enhances accuracy and robustness in human silhouette prediction. By combining initial silhouette estimates from a segmentation model with human joint predictions from a 2D pose estimation model, POISE leverages the complementary strengths of both approaches, effectively integrating precise body shape information and spatial information to tackle occlusions. Furthermore, the self-supervised nature of \POISE eliminates the need for costly annotations, making it scalable and practical. Extensive experimental results demonstrate its superiority in improving silhouette extraction under occlusions, with promising results in downstream tasks such as gait recognition. The code for our method is available https://github.com/take2rohit/poise.

翻译：人体轮廓提取是计算机视觉中的基础任务，广泛应用于各类下游场景。然而，遮挡问题常导致轮廓提取不完整或变形。针对这一挑战，我们提出POISE：姿态引导的遮挡条件下人体轮廓提取——一种新颖的自监督融合框架，可提升人体轮廓预测的准确性与鲁棒性。该方法将分割模型输出的初始轮廓估计与二维姿态估计模型预测的人体关节点相结合，充分发挥两种方法的互补优势，有效融合精确的人体形状信息与空间信息以应对遮挡。此外，POISE的自监督特性消除了昂贵标注的需求，使其具备可扩展性和实用性。大量实验结果表明，该方法在遮挡条件下显著提升了轮廓提取效果，并在步态识别等下游任务中展现出优异性能。方法代码已开源：https://github.com/take2rohit/poise