Detailed Garment Recovery from a Single-View Image
This addresses the need for practical garment modeling from existing photographs for applications in fashion, gaming, and virtual reality, representing a novel approach to a known bottleneck.
The paper tackles the problem of reconstructing detailed 3D garment models from single-view images, which is challenging due to limited input data, and achieves this by using statistical, geometric, and physical priors combined with parameter estimation and simulation, enabling applications like virtual try-on and cloth animation.
Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, we pro- pose a method that is able to compute a rich and realistic 3D model of a human body and its outfits from a single photograph with little human in- teraction. Our algorithm is not only able to capture the global shape and geometry of the clothing, it can also extract small but important details of cloth, such as occluded wrinkles and folds. Unlike previous methods using full 3D information (i.e. depth, multi-view images, or sampled 3D geom- etry), our approach achieves detailed garment recovery from a single-view image by using statistical, geometric, and physical priors and a combina- tion of parameter estimation, semantic parsing, shape recovery, and physics- based cloth simulation. We demonstrate the effectiveness of our algorithm by re-purposing the reconstructed garments for virtual try-on and garment transfer applications, as well as cloth animation for digital characters.