This invention discloses a method for fitting a 3D
human body model based on 2D contour interaction and semantic segmentation. The method first parses a user-drawn 2D
stick figure skeleton using a JPN module and drives an SMSLX model to generate an initial 3D
human body. Then, the initial model contour is rendered from multiple fixed perspectives, generating corresponding bounding
voxel spaces and
point cloud projections. A prior network combining UDF and positional information is introduced to guide the training of the multi-view semantic segmentation
network module UPS, achieving
accurate segmentation of each part of the contour image. Based on the segmentation results and
voxel point cloud projections, surface constraint point clouds for each body part are calculated. Finally, using the constraint point clouds as targets, the SMSLX
model parameters are optimized for each part using a CFM module, ultimately generating a 3D
human body shape that matches the user's editing intent. This invention allows users to indirectly adjust complex 3D models through intuitive 2D contour editing, significantly improving interaction efficiency and avoiding direct manipulation of complex 3D data.