The field of robotics and computer vision is rapidly advancing with the development of digital twins and 3D generation technologies. Researchers are exploring new methods to generate high-fidelity 3D models from single-view images, leveraging techniques such as contrastive learning and Gaussian splatting. These advancements have the potential to improve the quality and realism of synthetic data, enabling more effective training of computer vision models and enhancing robotic manipulation capabilities. Noteworthy papers in this area include ContrastiveGaussian, which achieves superior texture fidelity and improved geometric consistency in 3D generation, and RoboSplat, which generates diverse and visually realistic demonstrations for one-shot manipulation tasks. Digital twin technologies are also being applied to real-world scenarios, such as operating room workflow analysis and dual-arm robotic tasks, demonstrating significant potential for improving performance and success rates.