POISE: Pose Guided Human Silhouette Extraction under Occlusions
This addresses the challenge of occlusions in human silhouette extraction for computer vision applications, but it appears incremental as it builds on existing segmentation and pose estimation methods.
The paper tackles the problem of human silhouette extraction under occlusions by introducing POISE, a self-supervised fusion framework that combines segmentation and pose estimation, resulting in improved accuracy and robustness with promising results in tasks like gait recognition.
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.