The outfit testing using virtual try-on technology is increasingly in demand for online commercialization due to its flexibility and adaptability. With the advanced development in computer vision and three-dimensional (3D) modelling, it can measure the best fit of garment outfits for a digital human. The capture of physical humans and garments using scanning tools can be processed in point cloud, voxel, meshes and RGB-D format. Garment reconstruction is then processed by extracting clothing from existing 3D garment datasets or employing 2D-to-3D garment lifting techniques. The garment is applied to a human avatar, which is generally reconstructed using implicit function-based human reconstruction, neural radiance fields and 3D Gaussian Splatting techniques. By implementing generative adversarial networks or diffusion models, the garments can be transferred onto a new human body pose. This paper delves into three core components in virtual try-on technology, i.e. 3D garment generation, 3D human avatar reconstruction and body-garment transformation. A detailed quantitative comparison evaluates the effectiveness of digital avatar reconstruction and garment transformation methods based on outputs, datasets and performance metrics. Furthermore, this study explores practical application, identifies existing challenges, and outlines promising directions for future research. By providing an in-depth analysis of the current state of 3D VTO, this work serves as a valuable resource for advancing innovation in digital fashion technologies.
Fung et al. (Sun,) studied this question.