Multi-Modal Data Ingestion & Normalization Layer for Robust 3D Human Pose Estimation in Crowded Scenes

**Abstract:** This paper introduces a novel framework for improving the robustness and accuracy of 3D human pose estimation, particularly within densely populated and visually occluded environments. Current state-of-the-art methods often struggle with ambiguity arising from overlapping individuals and varying lighting conditions. Our `Multi-modal Data Ingestion & Normalization Layer (MDINL)` addresses this by integrating RGB imagery, depth maps, and skeletal information through a multi-layered evaluation pipeline, resulting in a significant improvement in pose estimation accuracy and scalability compared to existing approaches. The system's commercial viability stems from its practical implementation on existing embedded hardware platforms, enabling real-time applications in robotic assistance, surveillance, and interactive entertainment.