3D Human Shape Reconstruction from a Polarization Image
This addresses the problem of 3D human shape reconstruction for applications in computer vision and robotics, offering an incremental improvement by using polarization images as an alternative to conventional color or depth imaging.
This paper tackles the problem of estimating 3D body shape of clothed humans from single polarized 2D images by proposing a two-stage deep learning approach called SfP, which infers surface normal and reconstructs shape, demonstrating performance on synthetic and real-world datasets.
This paper tackles the problem of estimating 3D body shape of clothed humans from single polarized 2D images, i.e. polarization images. Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing surface normal of the objects of interest. Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images. A dedicated two-stage deep learning approach, SfP, is proposed: given a polarization image, stage one aims at inferring the fined-detailed body surface normal; stage two gears to reconstruct the 3D body shape of clothing details. Empirical evaluations on a synthetic dataset (SURREAL) as well as a real-world dataset (PHSPD) demonstrate the qualitative and quantitative performance of our approach in estimating human poses and shapes. This indicates polarization camera is a promising alternative to the more conventional color or depth imaging for human shape estimation. Further, normal maps inferred from polarization imaging play a significant role in accurately recovering the body shapes of clothed people.