Super-resolution 3D Human Shape from a Single Low-Resolution Image

256x256 input

SuRS front reconstruction

SuRS back reconstruction

We propose a novel framework to reconstruct super-resolution human shape from a single low-resolution input image. The approach overcomes limitations of existing approaches that reconstruct 3D human shape from a single image, which require high-resolution images together with auxiliary data such as surface normal or a parametric model to reconstruct high-detail shape. The proposed framework represents the reconstructed shape with a high-detail implicit function. Analogous to the objective of 2D image super-resolution, the approach learns the mapping from a low-resolution shape to its high-resolution counterpart and it is applied to reconstruct 3D shape detail from low-resolution images. The approach is trained end-to-end employing a novel loss function which estimates the information lost between a low and high-resolution representation of the same 3D surface shape. Evaluation for single image reconstruction of clothed people demonstrates that our method achieves high-detail surface reconstruction from low-resolution (256x256) images without auxiliary data. Extensive experiments show that the proposed approach can estimate super-resolution human geometries with a significantly higher level of detail than that obtained with previous approaches when applied to low-resolution images.
SuRS Approach




Marco Pesavento, Marco Volino, and Adrian Hilton, "Super-resolution 3D Human Shape from a Single Low-Resolution Image", IEEE European Conference on Computer Vision (ECCV), 2022.

  doi = {10.48550/ARXIV.2208.10738},
  url = {},
  author = {Pesavento, Marco and Volino, Marco and Hilton, Adrian},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Super-resolution 3D Human Shape from a Single Low-Resolution Image},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}