Single-Image to 3D Human: A Comprehensive Reconstruction Framework
Anaïs Druart · 2024
This paper presents a novel framework for the generation of highly realistic and controllable human digital twins. Leveraging advanced techniques in face and body modeling, the methodology integrates landmark detection, facial segmentation, and 3D reconstruction, drawing inspiration from state-of-the-art research in computer vision and deep learning. Results demonstrate the framework’s effectiveness in achieving lifelike representations while offering enhanced control over generated models. Furthermore, the seamless fusion of head and body components, coupled with streamlined animation processes, underscores the framework’s practical utility. This research contributes to the evolving landscape of digital twin generation, presenting a versatile approach with potential applications across various domains, including virtual reality, entertainment, and personalized avatar creation.