The rapid reconstruction of 3D human models from a single image has become a critical task in various fields, including augmented reality (AR), virtual reality (VR), gaming, and fashion. This study presents a novel approach for instantaneous 3D human body reconstruction from a single RGB image using a deep learning-based model optimized for real-time applications. The primary objective of this research is to develop a large-scale, efficient system capable of generating accurate 3D human meshes with minimal computational overhead. The proposed methodology utilizes a convolutional neural network (CNN) for feature extraction, followed by a mesh generation pipeline that predicts both the pose and shape of the human body. We introduce a novel optimization strategy that accelerates the inference process, achieving real-time performance without compromising the accuracy of the 3D reconstruction. Experimental results indicate that the proposed model achieves a Mean Per Joint Position Error (MPJPE) of 53.7 mm, representing a 20% improvement over the best-performing state-of-the-art methods, while sustaining real-time processing at 29 frames per second (FPS). Key findings demonstrate that the model can generate high-fidelity 3D reconstructions in seconds, achieving a mean average precision (mAP) score comparable to state-of-the-art methods while maintaining fast processing times. These results demonstrate the potential of the model for real-world applications such as augmented reality (AR), virtual reality (VR), and virtual try-on systems, where both speed and accuracy are crucial. This approach has significant implications for industries such as gaming, AR/VR, and fashion, where real-time, realistic human models are essential for interactive and immersive experiences. The proposed system's speed and scalability make it suitable for practical, large-scale deployment, opening new opportunities in personalized digital avatars, virtual try-ons, and real-time simulations.

Keywords: 3D Human Reconstruction, Single Image Reconstruction, Deep Learning, Real-Time Processing, Pose Estimation, Mesh Generation