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:this https URL
View on arXiv@article{melnik2025_2504.13159,
title={ Digital Twin Generation from Visual Data: A Survey },
author={ Andrew Melnik and Benjamin Alt and Giang Nguyen and Artur Wilkowski and Maciej Stefańczyk and Qirui Wu and Sinan Harms and Helge Rhodin and Manolis Savva and Michael Beetz },
journal={arXiv preprint arXiv:2504.13159},
year={ 2025 }
}