Garment Reconstruction

Garment reconstruction aims to create realistic, animatable 3D models of clothing from various input sources, such as multi-view videos or point cloud sequences. Current research heavily utilizes neural networks, including graph neural networks and neural radiance fields, often incorporating Gaussian map representations for detailed texture and surface modeling, and employing techniques like implicit surface representations and rigging models to handle complex garment dynamics. This field is significant for applications in virtual try-on, digital fashion, and animation, offering a more efficient and accurate method for creating realistic clothing assets compared to traditional manual modeling.