Digital Twin Generation from Visual Data: A Survey
It provides a comprehensive overview for researchers and practitioners, but is incremental as it synthesizes existing work.
This survey explores recent developments in generating digital twins from videos, analyzing various approaches and their implications for real-world applications like robotics and media creation.
This survey explores recent developments in generating digital twins from videos. Such digital twins can be used for robotics application, media content creation, or design and construction works. We analyze various approaches, including 3D Gaussian Splatting, generative in-painting, semantic segmentation, and foundation models highlighting their advantages and limitations. Additionally, we discuss challenges such as occlusions, lighting variations, and scalability, as well as potential future research directions. This survey aims to provide a comprehensive overview of state-of-the-art methodologies and their implications for real-world applications. Awesome list: https://github.com/ndrwmlnk/awesome-digital-twins