3D modelling is a technology in massive demand now and can potentially become a key factor for enabling subsequent technological evolutions such as metaverses, digital twins, and virtual reality. Current 3D modellings include high-precision 3D human body modelling and rapid modelling through single or multiple monocular photos. However, some problems persist in both modellings. The modelling based on high-precision equipment has low practicability, few applicable scenarios, and high cost. Modelling through monocular photos, on the other hand, has low accuracy and is sensitive to noisy data. And both modellings generate static 3D models. Therefore, to realize the model's dynamic effect in various fields while retaining fast modelling, we propose a system that recovers a 3D model from a single photo to fuse skeleton animation extracted from videos, for a realization of the Digital Twin (DT). DT is defined as "digital replications of living as well as non-living entities that enable data to be seamlessly transmitted between the physical and virtual worlds". Rigging is setting up the skeleton-based animation to combine the 3D model and skeleton animation. Traditional rigging method is time-consuming and non-reusable, since rigging is often done manually or semi-automatically. In this thesis, we propose an automatic rigging method to achieve a loose coupling fusion of one-to-many or many-to-one 3D models and skeletal animations. Our rigging method is fast and efficient, and only needs a single photo as input.