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 this https URL

POISE 是一种新颖的自监督融合框架，通过结合分割模型的初始轮廓估计和 2D 姿势估计模型的人体关节预测，有效地整合了精确的身体形状信息和空间信息来解决遮挡引起的不完整和扭曲的人体轮廓问题，在减少成本的自监督机制下，提高了轮廓提取准确性，同时在步态识别等下游任务中展现了优越性。