HumanPrinter: Reconstructing 3D Human from a Single Image Like a 3D Printer

High-fidelity 3D human reconstruction is essential for numerous applications. Existing reconstruction methods still suffer from several limitations. Implicit-function-based methods often produce artifacts, particularly when handling complex poses and loose-fitting clothing. Existing deformation-based methods require the entire body mesh to be input to the network for deformation, resulting in reconstructed results that are not ideal in detail. We propose HumanPrinter, a novel method for reconstructing high-fidelity 3D clothed human models from a single RGB image. Drawing inspiration from 3D printing, HumanPrinter reconstructs the human mesh layer by layer. HumanPrinter slices the coarse mesh resulting from deformation based on the estimated SMPL-X mesh into multiple vertically stacked polygons. The network then regresses vertex offsets from visual cues extracted from the input image to deform these polygons. Finally, these deformed polygons are stitched together and further refined to achieve a complete and detailed 3D human mesh. Polygon-based deformations associate the deformations of each vertex with its adjacent vertex so that the HumanPrinter produces fewer artifacts. By reducing the number of input polygons and increasing the number of deformable vertices, the layered reconstruction method can make the network more focused on local details. Through experiments on three datasets and visual results on in-the-wild data, we demonstrate that HumanPrinter performs competitive reconstruction quality compared to current state-of-the-art methods.