Sketch2Cloth: Sketch-based 3D Garment Generation with Unsigned Distance Fields
This addresses a domain-specific problem for designers and users in fashion or 3D modeling who need intuitive tools for garment creation from sketches.
The paper tackles the problem of generating 3D garment models from hand-drawn sketches, which is challenging for conventional methods due to non-watertight meshes, and proposes Sketch2Cloth, a system that uses unsigned distance fields to achieve this with quantitative evaluations against a state-of-the-art approach.
3D model reconstruction from a single image has achieved great progress with the recent deep generative models. However, the conventional reconstruction approaches with template mesh deformation and implicit fields have difficulty in reconstructing non-watertight 3D mesh models, such as garments. In contrast to image-based modeling, the sketch-based approach can help users generate 3D models to meet the design intentions from hand-drawn sketches. In this study, we propose Sketch2Cloth, a sketch-based 3D garment generation system using the unsigned distance fields from the user's sketch input. Sketch2Cloth first estimates the unsigned distance function of the target 3D model from the sketch input, and extracts the mesh from the estimated field with Marching Cubes. We also provide the model editing function to modify the generated mesh. We verified the proposed Sketch2Cloth with quantitative evaluations on garment generation and editing with a state-of-the-art approach.