Implicit Clothed Human Reconstruction Based on Self-attention and SDF

Recently, implicit function-based approaches have advanced 3D human reconstruction from a single-view image. However, previous methods suffer from problems such as artifacts, broken limbs, and loss of surface details when dealing with challenging poses. To address these issues, this paper proposes a novel neural network model based on PaMIR. Firstly, the Signed Distance Function (SDF) is introduced to define an implicit function, which improves the generalization ability of the model. Secondly, a feature volume encoding network with self-attention mechanism is designed to extract voxel-aligned features and provide richer geometric information, further improving the accuracy of shape topology structure. Through validation on the CAPE dataset, our method exceeds the PaMIR by 50.9% and 30.6% reduction in Chamfer and Point-to-Surface Distances respectively, and 18.2% reduction in normal estimation errors.