Construction of 3D Human Model from 2D Image using PyTorch and Blender

Abstract

In this study, we present our innovative approach to generating 3D models from 2D images, employing PyTorch, Blender, Python, and OpenCV. Our method integrates image pre-processing techniques, a pre-trained model, and rendering capabilities to produce high-fidelity 3D representations. Initially, the input images, stripped of backgrounds using OpenCV, undergo pre-processing steps to enhance features relevant for 3D reconstruction, such as edge detection and depth estimation. We utilize PyTorch, a deep learning framework, to implement a leading edge model trained on large-scale 3D datasets, enabling accurate conversion of 2D imagery into 3D structures. Subsequently, the generated 3D model is imported into Blender, a powerful 3D modeling software, for refinement and further enhancements. Python scripts facilitate the integration between PyTorch, OpenCV, and Blender, streamlining our workflow for seamless data exchange and processing. Finally, the reconstructed 3D model, generated from input images devoid of backgrounds, is rendered as a video in MP4 format, showcasing its dynamic attributes and spatial properties. This comprehensive methodology offers a robust framework for generating realistic 3D models from background-removed 2D images, with broad applications in computer vision, virtual reality, and digital content creation.