  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.
